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A partial Channel State Information (CSI) model for orthogonal frequency division multiplexed (OFDM) transmissions over Multi-input Multi-Output (MIMO) frequency selective fading channels is discussed. An adaptive MIMO-OFDM transmitter is also developed by applying an adaptive 2-D coder-beamformer.

An optimal transmistter design for multiple-antenna systems is developed. It is based on channel mean feedback.

Space-time coding is a well-documented, widely adopted means of achieving high data-rate transmissions. A subspace-based blind channel estimator for block-precoded space-time OFDM transmissions via frequency-selective channels is described.

In this paper, we investigate various channel estimators that exploit channel sparsity in the time and/or Doppler domain for a multicarrier underwater acoustic system. We use a path-based channel model, where the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor, and derive the exact inter-carrier- interference (ICI) pattern. For channels that have limited Doppler spread we show that subspace algorithms from the array processing literature, namely Root-MUSIC and ESPRIT, can be applied for channel estimation. For channels with Doppler spread, we adopt a compressed sensing approach, in form of Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) algorithms, and utilize overcomplete dictionaries with an increased path delay resolution. Numerical simulation and experimental data of an OFDM block-by-block receiver are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator. We observe that subspace methods can tolerate small to moderate Doppler effects, and outperform the LS approach when the channel is indeed sparse. On the other hand, compressed sensing algorithms uniformly outperform the LS and subspace methods. Coupled with a channel equalizer mitigating ICI, the compressed sensing algorithms can effectively handle channels with significant Doppler spread. [ABSTRACT FROM AUTHOR]

In decentralized detection, local sensor observations have to be communicated to a fusion center through the wireless medium, inherently a multiple-access channel (MAC). As communication is bandwidthand energy-constrained, it has been suggested to use the properties of the MAC to combine the sensor observations directly on the channel. Although this leads to an array-processing gain if the sensors' transmissions combine coherently on the channel, it has been shown that this is not the case when they combine noncoherently. We review known results for the coherent case and then analyze the noncoherent case based on a simple on/off scheme combined with optimal sensor "censoring." Since the optimal forwarding function is not available, we also bound the performance using an equivalent communication problem and a centralized estimator to verify trends. We find that for noncoherent modulation, there is no processing gain using the MAC for decentralized detection, but compared to parallel-access channels (PACs) the MAC avoids the noncoherent combining loss. Still the performance of the MAC approach is only of diversity one, as the output of the MAC is approximately a zero-mean complex Gaussian random variable for a large number of sensor. The MAC performance can be increased by using multiple independent channels, each used as a MAC by all sensors, which we term diversity-MAC. This approach always outperforms the PAC scheme on Rayleigh fading channels, where the output is exactly Gaussian, but has inferior performance across random phase channels when few sensors are used, as the PAC does not create "artificial" fading. [ABSTRACT FROM AUTHOR]