Low Complexity Blind Equalization for OFDM Systems with General Constellations

This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm

BEM-based SISO detection of orthogonal space-time block codes over frequency flat-fading channels

The expectation-maximization algorithm for maximum a posteriori (MAP) estimation of a random vector is applied to the problem of detection of orthogonal space-time block codes over time-selective Rayleigh fading channels. This results in a soft-in soft-out detection algorithm suitable for iterative detection/decoding schemes. Simulation results show that the error performance of the proposed detection algorithm is very close to that of a MAP detector endowed with an ideal knowledge of the channel state if the fading rate is not too fast

Performance Analysis of MIMO-STBC Systems with Higher Coding Rate Using Adaptive Semiblind Channel Estimation Scheme

Semiblind channel estimation method provides the best trade-off in terms of bandwidth overhead, computational complexity and latency. The result after using multiple input multiple output (MIMO) systems shows higher data rate and longer transmit range without any requirement for additional bandwidth or transmit power. This paper presents the detailed analysis of diversity coding techniques using MIMO antenna systems. Different space time block codes (STBCs) schemes have been explored and analyzed with the proposed higher code rate. STBCs with higher code rates have been simulated for different modulation schemes using MATLAB environment and the simulated results have been compared in the semiblind environment which shows the improvement even in highly correlated antenna arrays and is found very close to the condition when channel state information (CSI) is known to the channel

Bayesian Modeling For Dealing With Uncertainty In Cognitive Radios

Wireless communication systems can be affected by several factors, including propagation losses, co-channel interference, and multipath fading. Uncertainty affects all of these factors making it even more difficult to model these systems. This dissertation proposes the use of probabilistic graphical models (PGM), such as Bayesian Networks and Influence Diagrams, as the core for reasoning and decision making in adaptive radios operating under uncertainty. PGM constitute a tool to understand and model complex relations among random variables. This dissertation explains how to build effective communication models that perform its functions under uncertainty. In addition, this work also presents a spectrum sensing technique based on the autocorrelation of samples to estimate the utilization level of wireless channels