Optimal training and redundant precoding for block transmissions with application to wireless OFDM

2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689)

2003

In a multiple access communication system that uses packet transmissions, the packets of one user might be subject to asynchronous interference from the packets of other users in the system. This paper analyses the influence of the placement of training symbols on the performance of the channel estimator in this scenario. The analysis of the mean square error (MSE) of the minimum mean square error estimator (MMSE) is shown to be equivalent to the analysis of the Fisher Information Matrix (FIM) for mixtures of Gaussian distributions. A complete solution to this problem is hard to find, but the bounds, asymptotics and simulations suggest that the best placement of training symbols is in two clusters of equal or quasi-equal size at the two ends of the data packet.

“…The convexity property given above allows us to reduce the number of possible solutions of the optimization problem (10). Using the symmetry properties in (3), we can rewrite the pdf in .…”

Section: A Partial Characterization Of the Msementioning

confidence: 99%

Section: Conjecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Training symbol placement for packet transmissions under asynchronous interference

Budianu

2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689)

2003

“…Optimal training has been previously considered for block fading channels from a channel estimation perspective under both TDM and superimposed trainings [9,5,2] and from an information theoretic angle in [10,1]. For time-varying channels, Cavers first analyzed the pilot symbol assisted modulation (PSAM) [3] that includes the periodic TDM training with cluster size one-the RPP-1 placement.…”

Section: Introductionmentioning

confidence: 99%

Optimal pilot placement for time-varying channels

Dong

2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689)

Sadler³

2003

Two major training techniques for wireless channels are the timedivision multiplexed (TDM) training and the superimposed training. For the TDM schemes with regular periodic placements (RPP), the closed-form expression of the steady-state minimum mean square error (MMSE) is obtained as a function of pilot placement for Gauss-Markov fading channels. We show that the single cluster RPP scheme (RPP-1) minimizes the maximum steady-state channel MMSE. For BPSK and QPSK signaling, we then show that the optimal placement that minimizes the maximum bit error rate (BER) is also RPP-1. We next compare the MMSE and BER performance under the superimposed training schemes with those under the optimal TDM scheme. It is shown that while RPP-1 scheme performs better at high SNR and for slow varying channels, the superimposed scheme outperforms RPP-1 in the other regimes. This demonstrates the potential for using superimposed training in relatively fast time-varying environments.

“…The second reason comes from the placement of pilot symbols in clusters suitable only for training-based techniques. It has been established recently that placing and designing pilot symbols optimally provides gain in channel capacity [8]- [10] and reduction of symbol and channel estimation errors [11]- [14]. A more general form of training that allows the superposition of pilot and data symbols has attracted attention recently [10], [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for space-time orthogonal block codes

Budianu

ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240)

Abstract-Channel estimation is one of the key components of space-time systems design. The transmission of pilot symbols, referred to as training, is often used to aid channel acquisition. In this paper, a class of generalized training schemes that allow the superposition of training and data symbols is considered. First, the Cramér-Rao lower bound (CRLB) is derived as a function of the power allocation matrices that characterize different training schemes. Then, equivalent training schemes are obtained, and the behavior of the CRLB is analyzed under different power constraints. It is shown that for certain training schemes, superimposing data with training symbols increases CRLB, and concentrating training power reduces CRLB. On the other hand, once the channel is acquired, uniformly superimposed power allocation maximizes the mutual information and, hence, the capacity.