Full-Hardware Architectures for Data-Dependent Superimposed Training Channel Estimation

Abstract

Channel estimation based on superimposed training (ST) has been an active research topic around the world in recent years, because it offers similar performance when compared to methods based on pilot assisted transmissions (PAT), with the advantage of a better bandwidth utilization. However, physical implementations of such estimators are still under research, and only few approaches have been reported to date. This is due to the computational burden and complexity involved in the algorithms in conjunction with their relative novelty. In order to determine the suitability of the ST-based channel estimation for commercial applications, the performance and complexity analysis of the ST approaches is mandatory. This work proposes two full-hardware channel estimator architectures for a data-dependent superimposed training (DDST) receiver with perfect synchronization and nonexistent DC-offset. These architectures were described using Verilog HDL and targeted in Xilinx Virtex-5 XC5VLX110T FPGA. The synthesis results of such estimators showed a consumption of 3 % and 1 % of total slices available in the FPGA and frequencies operation over 160 MHz. They have also been implemented on a generic 90 nm CMOS process achieving clock frequencies of 187 MHz and 247 MHz while consuming 3.7 mW and 2.74 mW, respectively. In addition, for the first time, a novel architecture that includes channel estimation, training/block synchronization and DC-offset estimation is also proposed. Its fixed-point analysis has been carried out, allowing the design to produce practically equal performance to those achieved with the floating-point models. Finally, the high throughputs and reduced hardware consumptions of the implemented channel estimators, leads to the conclusion that ST/DDST can be utilized in practical communications systems.