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指導老師 : 王瑞騰 老師 學生 : 徐穎千

Tracking Performance of Least Squares MIMO Channel Estimation Algorithm IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 55, NO. 11, NOVEMBER 2007. 指導老師 : 王瑞騰 老師 學生 : 徐穎千. Outline. Introduction System model RLS channel estimation algorithm Tracking analysis Simulation Conclusion References.

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指導老師 : 王瑞騰 老師 學生 : 徐穎千

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  1. Tracking Performance of Least Squares MIMO Channel Estimation AlgorithmIEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 55, NO. 11, NOVEMBER 2007 指導老師:王瑞騰 老師 學生:徐穎千

  2. Outline • Introduction • System model • RLSchannel estimation algorithm • Tracking analysis • Simulation • Conclusion • References

  3. Introduction • The LS algorithm, which is independent of the channel model , is commonly used in equalization and filtering applications . • The LS algorithm is proposed in [1], [2] as a MIMO channel estimator that does not require the exact model of the channel to be known and whose complexity is only of the order of 3 [1]. • In this paper, by analyzing the tracking algorithm proposed in [1], the channel-tracking error is derived as a closed-form function, and is used to evaluate and optimize the algorithm.

  4. System Model

  5. The observable signal rikfrom the receiver i (with i = 1, . . . , N) at discrete time index k is • Above equation can be written in a matrix form as • The time-varying behavior of the channel matrix can be described as the following Vk is a matrix with i.i.d. Rayleigh elements with variance σV2 .

  6. RLSChannel Estimation Algorithm • In the LS algorithm , the cost function must be defined as a weighted average of error squares • is the estimate of • 假設

  7. Channel estimation algorithm: • Initialize the parameters • Update Rn and Qn by and • Calculate the channel matrix estimation by and return to 2) in the next snapshot.

  8. Tracking Analysis • Channel estimation error : • The variance of the channel tracking error : presents the tracking performance of the LS estimation for the flat fading MIMO channel .

  9. Simulation

  10. Conclusion • The presented tracking error is compared to the tracking error computed through Monte Carlo simulation for QPSK-based training signals, and it is shown that simulation and analysis have a good match. • Of course, the analytical results that are related to the LS algorithm are compared to the simulation results of the RLS algorithm that avoids matrix inversion. • Finally, by minimization of the error function versus forgetting factor, it is shown that the optimum value of the forgetting factor is highly dependent on the training length, Doppler shift, and Eb/N0 .

  11. References [1] E.Karami and M. Shiva, “Decision-directed recursive least squares MIMO channels tracking,” EURASIP J. Wireless Commun. Netw., vol. 2006,pp. 1–10, 2006. [2] E. Karami, M. Shiva, and M. Khansari, “An efficient MIMO receiver structure for coded signals,” in Proc. 57th IEEE Veh. Technol. Conf.,.Jeju, South Korea, Apr. 22–25, 2003, vol. 2, pp. 1089–1093. [3] G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Pers.Commun., vol. 6, no. 3, pp. 311–335, Mar. 1998. [4] V. Pohl, P. H. Nguyen, V. Jungnickel, and C. Von Helmolt, “How often channel estimation is needed in MIMO systems,” in Proc. IEEE Global Telecommun. Conf., Dec. 2003, no. 1, pp. 814–818.

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