Joint Frequency Offset Estimation and Multiuser Detection Using Genetic Algorithm in MC-CDMA Hoang-Yang Lu
Wen-Hsien Fang
Dept. of Electronic Engineering Lee-Ming Institute of Technology Taipei, Taiwan, R.O.C. Email:
[email protected]
Dept. of Electronic Engineering National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. Email:
[email protected]
Abstract— In order to simultaneously combat both of the inter-carrier interferences (ICIs) and multiple access interferences (MAIs) to achieve reliable performance in multi-carrier code division multiple access (MC-CDMA) systems, this paper proposes a new scheme for joint frequency offset estimation and multiuser symbol detection. The new approach is based on the widespread maximum likelihood principle to concurrently estimate the frequency offsets to alleviate the ICIs and carry out multiuser detection to mitigate the MAIs. The joint decision statistic, however, is highly nonlinear and the conventional linear schemes are not applicable. To reduce the computational complexity called for without an increase of additional mechanisms, we employ the genetic algorithm (GA) to solve the nonlinear optimization involved. Due to the robustness of the GA, the joint decision statistic can be efficiently solved and near optimum results can be obtained. Furnished simulation results show that the proposed approach offers satisfactory performance in various scenarios.
I. I NTRODUCTION MC-CDMA, which possesses the merits of spectral efficiency of CDMA systems and of robustness against intersymbol interference (ISI) of orthogonal frequency division multiplexing (OFDM) systems [1], is a promising candidate in the third generation (3G) or future wireless communication systems. However, a key disadvantage in MC-CDMA is the carrier frequency offset, resulting from the Doppler shift or the mismatch of the carriers between the transmitter and receiver. The frequency offset will cause the inter-carrier interferences (ICIs), which then substantially degrade the system performance. Several approaches [2], [3], [4] have been addressed to overcome this drawback. For example, [2] uses the correlation between the received streams and the corresponding spreading codes to find the frequency offset. However, as the fading effect increases, the estimation accuracy will deteriorate considerably. On the other hand, [3], [4] need either additional pilots or hardware blocks such as phase lock loop (PLL) to mitigate the ICIs, which will in turn degrade the data transmission rate and call for extra cost for the synchronization mechanism. Also, when MC-CDMA suffers from multiple access interferences (MAIs), there will be severe performance deterioration. The multiuser detection (MUD) [5] is a widespread technique to alleviate the MAIs. Based on the maximum
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likelihood (ML) approach, the computational complexity required by the optimum MUD increases exponentially with the number of users and is computationally prohibitive in practice. Therefore, several sub-optimum schemes such as minimum mean squared error (MMSE) MUD, successive interference cancellation (SIC), and parallel interference cancellation (PIC) have received lots of attention. However, in broadband systems, to keep the transmission quality at a guaranteed level when the fading channel becomes more destructive, the ML scheme is still a favored approach. In order to simultaneously combat both of the ICIs and MAIs to achieve reliable performance in MC-CDMA systems, a joint frequency offset estimation and symbol detection scheme is proposed in this paper. The new approach is based on the ML principle to concurrently estimate the frequency estimates to alleviate the ICIs and carry out the MUD to mitigate the MAIs. The joint decision statistic, however, becomes highly nonlinear in this scenario and the conventional linear schemes are not applicable. To resolve the computational dilemma induced by the nonlinear optimization, we employ the genetic algorithm (GA) to solve this problem. The GA is an iterative approach which refines the estimates through a sequence of evolution processes and has been successfully employed in various facets of signal processing such as control, cellular automata, pattern recognition, etc. [6]. Due to the effectiveness and robustness of the GA, the joint decision statistic can then be efficiently solved and near optimum results can be obtained. Conducted simulation results show that the proposed approach yields satisfactory performance with reduced computational overhead compared with the traditional ML approach in various scenarios. II. S IGNAL M ODEL Assume that there are K active users in an uplink MCCDMA system in which each user is assigned a unique spreading code ck , k = 1 · · · K, with a processing gain M . The transmitted data of each user is BPSK modulated and is assumed to be independent and identically distributed. The modulated signals are then spread and an M -point inverse fast Fourier transform (IFFT) is taken to translate them into time sequences. Also, we assume that enough guard intervals are appended into the sequences to combat the intersymbol
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interferences and that there is a frequency offset ∆fk between the base station and the k th mobile, k = 1 · · · K, so the normalized frequency offset can be expressed as k = ∆fk Tb , where Tb is the symbol duration. After conducting FFT and some matrix stacking manipulations, the M × 1 received discrete vector x(i) in the ith symbol duration can be expressed as [3] 1 x(i) = BYSFAd(i) + n(i) (1) M
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