Md. Shah Afran

On the Performance of Equalization Techniques for Aeronautical Telemetry

This paper compares the performance of the zero forcing (ZF), minimum mean-squared error (MMSE), and MMSE-initialized CMA+AMA equalizers using SOQPSK-TG over multipath channels captured during channel sounding experiments at Edwards AFB. The ZF and MMSE equalizers are data-aided and are based on estimates of the channel conditions derived from the preamble and ASM bits included in each iNET packet. The simulated bit error rate results show that MMSE and CMA+AMA achieve similar performance and are both about 1 -- 3 dB better than the ZF equalizer. Comparison with the optimum AWGN detector shows that 4 -- 9 dB of link margin is required to account for the multipath interference.

Equalization in aeronautical telemetry using multiple transmit antennas

This paper introduces a generalized time-reversed space-time block code (GTR-STBC) for single-carrier systems employing multiple transmit antennas and operating over frequency-selective multipath fading channels. The generalization involves unequal power allocation parameterized by ρ. The criteria for selecting the optimum ρ is minimizing the postequalizer mean-squared error for a minimum mean-squared error (MMSE) equalizer. GTR-STBC is applied to measured channel impulse responses and a simple statistical channel model. The results show that the optimum value of ρ gives the best trade-off between signal-to-noise ratio and intersymbol interference (ISI); equal power allocation may not be the optimum power allocation when channel state information is available; and the optimum profile of ρ over measured channels is significantly different than from that in statistical channel models.

A polynomial rooting based ML CFO estimator in multipath channel with repetitive pilot symbols

In this paper, we introduce a polynomial rooting based method for data-aided carrier frequency offset (CFO) estimation in the presence of frequency selective multipath fading. This estimator follows the maximum-likelihood (ML) criterion and offers a reduction in computational complexity when the pilot symbols are repetitive. Our analysis shows that depending on the number of repetitions and the length of each repetition, polynomial rooting based ML CFO estimator could be more numerically robust than search based ML CFO estimator. It is also observed that the polynomial rooting based CFO estimator achieves the Cramér-Rao bound at moderately low signal-to-noise ratio.