Pilot symbol transmission for time-varying fading channels: an information-theoretic optimization

We consider the optimal design of pilot-symbolassisted modulation (PSAM) in time-varying flat-fading channels (FFC). The FFC is modeled as an autoregressive Gauss-Markov random process, whose realization is unknown at the transmitter or at the receiver. Our measure of optimality for channel estimation is the rate of information transfer through the channel. The parameters that are available for this optimization are the power ratio and the fraction of time that are allocated to pilot transmission. Our approach is different from (and builds upon) a recent study of PSAM for the Gauss-Markov FFC, where the aim was to minimize the maximum steady-state minimum mean square error (MMSE) of channel estimation for equal power allocated to pilot and data symbols and for a fixed pilot insertion ratio. To this end, we examine a lower bound on the capacity and find the optimal pilot transmission parameters that maximize this bound. Our analysis shows that this capacity lower bound is more sensitive to the pilot power allocation ratio in relatively slow fading channels (with the normalized fading rate fDT8~0.01).We observe that for such slow fading rates, optimal power allocation and optimal pilot spacing are more sensitive to the operating SNR. Another finding is that equal power allocation strategy is suboptimal by at most 1 dB for the slow fading rate of fDT8~0.0

Training Optimization for Gauss-Markov Rayleigh Fading Channels

In this paper, pilot-assisted transmission over Gauss-Markov Rayleigh fading channels is considered. A simple scenario, where a single pilot signal is transmitted every T symbols and T-1 data symbols are transmitted in between the pilots, is studied. First, it is assumed that binary phase-shift keying (BPSK) modulation is employed at the transmitter. With this assumption, the training period, and data and training power allocation are jointly optimized by maximizing an achievable rate expression. Achievable rates and energy-per-bit requirements are computed using the optimal training parameters. Secondly, a capacity lower bound is obtained by considering the error in the estimate as another source of additive Gaussian noise, and the training parameters are optimized by maximizing this lower bound.Comment: To appear in the Proc. of the 2007 IEEE International Conference on Communication

Blind Estimation of Multiple Carrier Frequency Offsets

Multiple carrier-frequency offsets (CFO) arise in a distributed antenna system, where data are transmitted simultaneously from multiple antennas. In such systems the received signal contains multiple CFOs due to mismatch between the local oscillators of transmitters and receiver. This results in a time-varying rotation of the data constellation, which needs to be compensated for at the receiver before symbol recovery. This paper proposes a new approach for blind CFO estimation and symbol recovery. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response is estimated and used to subsequently transform the multiple CFOs estimation problem into many independent single CFO estimation problems. Furthermore, an initial estimate of the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao Lower bound is also derived, and the large sample performance of the proposed estimator is compared to the bound.Comment: To appear in the Proceedings of the 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Athens, Greece, September 3-7, 200