Conference Paper

# Error-entropy based channel state estimation of spatially correlated MIMO-OFDM.

University of New South Wales, Sydney, Australia

DOI: 10.1109/ICASSP.2011.5947132 Conference: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic Source: DBLP

- Citations (15)
- Cited In (0)

- [Show abstract] [Hide abstract]

**ABSTRACT:**This paper deals with the capacity behavior of wireless orthogonal frequency-division multiplexing (OFDM)-based spatial multiplexing systems in broad-band fading environments for the case where the channel is unknown at the transmitter and perfectly known at the receiver. Introducing a physically motivated multiple-input multiple-output (MIMO) broad-band fading channel model, we study the influence of physical parameters such as the amount of delay spread, cluster angle spread, and total angle spread, and system parameters such as the number of antennas and antenna spacing on ergodic capacity and outage capacity. We find that, in the MIMO case, unlike the single-input single-output (SISO) case, delay spread channels may provide advantages over flat fading channels not only in terms of outage capacity but also in terms of ergodic capacity. Therefore, MIMO delay spread channels will in general provide both higher diversity gain and higher multiplexing gain than MIMO flat fading channelsIEEE Transactions on Communications 03/2002; · 1.75 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Multiple-antenna wireless communication links promise very high data rates with low error probabilities, especially when the wireless channel response is known at the receiver. In practice, knowledge of the channel is often obtained by sending known training symbols to the receiver. We show how training affects the capacity of a fading channel-too little training and the channel is improperly learned, too much training and there is no time left for data transmission before the channel changes. We compute a lower bound on the capacity of a channel that is learned by training, and maximize the bound as a function of the received signal-to-noise ratio (SNR), fading coherence time, and number of transmitter antennas. When the training and data powers are allowed to vary, we show that the optimal number of training symbols is equal to the number of transmit antennas-this number is also the smallest training interval length that guarantees meaningful estimates of the channel matrix. When the training and data powers are instead required to be equal, the optimal number of symbols may be larger than the number of antennas. We show that training-based schemes can be optimal at high SNR, but suboptimal at low SNR.IEEE Transactions on Information Theory 05/2003; · 2.62 Impact Factor -
##### Conference Paper: Optimization of training sequences for spatially correlated MIMO-OFDM.

[Show abstract] [Hide abstract]

**ABSTRACT:**The optimal training sequence for channel estimation in spatially correlated multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems has not been found for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNR. Provided in this paper are (i) a necessary and sufficient condition for the optimal training sequence together with a convex programming to find the solution, and (ii) efficient procedures to find the optimal training sequence. Simulation results confirms the superiority of the proposed design over the existing one.Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 19-24 April 2009, Taipei, Taiwan; 01/2009

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.