Performance improvement in detection and estimation of MC-CDMA systems over MIMO channels
Introduction
The increasing need to transmit at high data rates and to increase the capacity over wireless channels have motivated the development of multi-carrier code division multiple access (MC-CDMA) based multiple-input multiple-output (MIMO) systems for next generation mobile communication. MC-CDMA has emerged as a promising technique for future wireless systems. This owes to the fact that it combines orthogonal frequency division multiplexing (OFDM) and CDMA [1]. MC-CDMA systems additionally employs multiple-input multiple-output (MIMO) techniques to increase the capacity of wireless link significantly by combining the advantages of spatial diversity which enable different users to share the available spectrum [2]. However, capacity of the system is limited by multiple access interference (MAI) from other users. By providing near-far resistance at the receiver and by utilising known user spreading codes, multi-user detection (MUD) techniques emerged to overcome the inherent shortcomings of MAI [3].
Optimal MUD has exponential computational complexity and is therefore impractical [4]. Several low complexity MUD such as decorrelator, minimum mean square error (MMSE) successive interference cancellation (SIC) and parallel interference cancellation (PIC) [5], [6] have been proposed. The decorrelator detector eliminates the correlation between different users by restoring their orthogonality and by doing so they might amplify noise [5]. Noise amplification was constrained in MMSE MUD by utilising Wiener filter criterion [7]. SIC is more popular [8] as it has strong error-correcting codes and can achieve impressive spectral efficiency under ideal conditions. SIC based MMSE detection technique for MC-CDMA with frequency domain equalisation proposed in Ref. [9] reduces interference among users by restoring orthogonality among spreading sequences. Binary genetic algorithm based MUD for MC-CDMA system was proposed [10] with less computational complexity. Particle swarm optimisation MUD for MC-CDMA system was put forward in [11] outperforms genetic algorithm based MUD with same computational complexity.
Adopting the advantages of SIC space time serial interference cancellation (STSIC) based MUD for MIMO MC-CDMA system is proposed to reduce MAI. STSIC receiver exploits receiver diversity by combining the received signals from each antenna using maximum ratio combining (MRC) technique and SIC. Moreover, analysis is also performed over spreading codes, with the usage of random codes and Gold codes of different lengths.
For the estimation case, the system requires channel state information (CSI) at the receiver. Hence, pilot symbols are often periodically inserted into the transmitted signal to support channel estimation (CE). Initially, CE is performed using least-squares algorithm on pilots only; and is interpolated with localised estimates (LE) on time frequency grid, exploiting the correlations of time and frequency selective radio channel. In case of pilot aided channel estimation (PACE), interpolation is achieved by cascading two one-dimensional finite-impulse response filters whose coefficients are based on MMSE criterion [12]. To improve PACE further, previously decided data symbols are used as reference in iterative channel estimation (ICE) [13]. PACE using overlap frequency domain equalisation [14] is proposed without insertion of guard interval. Maximum aposteriori CE based on expectation maximisation method was introduced in Ref. [15] to improve its performance. ICE algorithm for OFDM is designed to feedback the information from output of the channel decoder to estimation stage to reduce decision feedback errors. Since CE gets additional information from the estimated data symbols, ICE achieves a further reduction of bit error rate (BER). ICE is utilised in MIMO MC-CDMA system [16] to obtain better performance. They employ orthogonal STBCs built with the aid of Walsh–Hadamard (W–H) spreading codes at the transmitter [17].
Turbo coding seeks widespread applications over wireless communication for its impressive coding gain and has the capability to work in harmony with MC-CDMA system. A low complexity non-linear matched filter utilising turbo processing [18] is proposed for MC-CDMA for CE. Furthermore, in this paper turbo aided ICE is proposed to improve the estimation of MC-CDMA system by reducing their error rates.
Section snippets
MUD in MC-CDMA
MUD process in MC-CDMA systems can be classified into three as optimal MUD, linear MUD and non-linear MUD. The optimal MUD simultaneously detects all users’ data to jointly minimise the effects of MAI. The optimal MUD is the maximum likelihood (ML) receiver that yields the optimal estimate of the transmitted data and is given bywhere
argmax{.} is the argument maximising the expression,
is the data to be maximised,
is the Hermitian
System model
The block diagram of MIMO MC-CDMA system with turbo aided ICE transmitter is shown in Fig. 2. The input bits from single user out of K active users are turbo encoded using parallel concatenation of two ½ rate recursive systematic convolutional encoders separated by a block interleaver. They are basically simple error-correcting codes and are used to improve the SNR of the system. The coded data has higher data rate than the input bits from the user and is interleaved using a random bit
Simulation results
The proposed MUD and CE of MIMO MC-CDMA system is simulated using MATLAB. For detection purpose, it is assumed that NT independent data streams of one user spreaded by either Gold codes of length 31 and 63 or random codes of length 31 and 63 and are transmitted through NT transmit antennas. A frame size of M = 4 is used and interferers are assumed to have uniform power distribution among them. The delay in estimation errors is modeled under Rayleigh fading channel with independent zero-mean
Conclusion
STSIC based detection and turbo aided ICE is proposed for MC-CDMA system over MIMO channel to combat the affects of multi-path fading. The STSIC method outperforms optimal MUD and MMSE MUD by overcoming the affects of MAI with increased system performance by exploiting receiver diversity and SIC. Furthermore, results show that the performance of the system increases with the increase in number of transmit and receive antennas. Use of Gold code in STSIC is best suited than random codes in
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