• Journal of information and communication convergence engineering
• /
• v.16 no.1
• /
• pp.17-22
• /
• 2018

In massive multiple-input multiple-output (MIMO) systems, linear channel estimation algorithms are widely applied owing to their simple structures. However, they may cause pilot contamination, which affects the subsequent data detection performance. Therefore, herein, for an uplink multicell massive multiuser MIMO system, we consider using an alternating projection (AP) for channel estimation to eliminate the effect of pilot contamination and improve the performance of data detection in terms of the bit error rates as well. Even though the AP is nonlinear, it iteratively searches the best solution in only one dimension, and the computational complexity is thus modest. We have analyzed the mean square error with respect to the signal-to-interference ratios for both the cooperative and non-cooperative multicell scenarios. From the simulation results, we observed that the channel estimation results via the AP benefit the following signal detection more than that via the least squares for both the cooperative and non-cooperative multicell scenarios.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.12 no.1
• /
• pp.97-105
• /
• 2009

In this paper, the performance of AP(Alternating Projection) and EM(Expectation Maximization) algorithms is investigated in terms of detection of multiple signals, resolvability of coherent signals and the efficiency of sensor array processing. The basic idea of these algorithms is utilization of relaxation technique of successive 1D maximization to solve a direction finding problem by maximizing the multidimensional likelihood function. It means that the function is maximized over only for a single parameter while the other parameters are fixed at each step of the iteration. According to simulation results, the algorithms showed good performance for both incoherent and coherent multiple signals. Moreover, some advantages are identified for direction finding with very small samples and fast convergence. The performance of AP algorithm is compared with that of EM using multiple criteria such as the number of sensor, SNR, the number of samples, and convergence speed over uniform circular array. It is resulted AP algorithm is superior to EM overally except for one criterion, convergence speed. Especially, for EM algorithm there is no performance difference between incoherent and coherent case. In conclusion, AP and EM are viable and practical alternatives, which can be applied to a direction under due to the resolvability of multi-path signals, reliable performance and no troublesome eigen-decomposition of the sample-covariance matrix.