gure 3, we compare the performance of the IDMA algorithm, with different random values of hk(j) for k = 1, …, K; j = 1, …, J; in two different cases:
• Without estimation block: the coefficients of the channel are fixed as hk(j) = 1 k, j at the receiver and the algorithm in section 2 is applied.
• With blind estimation: the channel coefficients are blindly estimated, with three estimation iterations.
The Figure 3 illustrates the mean Bit Error Rate (BER) versus signal-to noise ratio (SNR = Eb = N0), where Eb is the mean user bit signal power.
Figure 2. Turbo Decoder with fine estimation.
Figure 3. BER versus SNR performance exhibited by the iterative turbo receiver without and with blind channel estimation.
4.2. Time-Varying Channel
Figure 4 shows the performance of the proposed iterative algorithm in time varying channels when = 0.001 (i.e. while subject to a random walk of ±25% over the whole frame) and give a comparison with the detection under perfect CSI at receiver side, for J = 256 × 8 chips and the performance exhibited by the iterative turbo receiver of Figure 2.
We illustrate, in Figure 5 the performance of the system under the same simulation conditions with = 0, 01. In Figure 6, the evolution of the BER for variable standard deviation from = 0.0001 to = 0.1 is illustrated, in the same simulation conditions than above. We observe good performance for = 0.0001 until = 0.001, and poor performance for > 0.01.
4.3. Trajectory Channel Variation
Figures 7 and 8 illustrate the evolution of estimated channel coefficients using the iterative algorithm with five pilot sequences (SA = 5) and without any pilot sequence, respectively and also provide the exact trajectory of coefficient h4
In this paper we proposed low complexity iterative joint channel estimation, and decoding scheme for IDMA systems. This method has been developed to extract all the data/channel information through a two-level iteration algorithm. The proposed system inherits the low-complexity advantage of IDMA technique. In this paper, we have documented the performance trends exhibited by the proposed turbo detection receiver. The results obtained
Figure 4. BER versus SNR for channel time-varying with SA = 5 and σw = 0.001. For M = 5 (decoding iterations) and L = 0; 1; 2; 3 (channel estimation iteration).
Figure 5. BER versus SNR for channel time-varying with SA = 5 and σw = 0.01. For M = 5 (decoding iterations) and L = 0; 1; 2; 3 (channel estimation iteration).
Figure 6. The BER versus SNR performance exhibited by turbo detection. The pilot bits with SA = 5 and the iteration pattern (M; L; N) = (5; 3; 15).
Figure 7. Sample trajectory of h4 at SNR = 5 dB, σw = 0:01 with channel estimation (SA = 5 bits).
Figure 8. Sample trajectory of h4 at SNR = 5 dB, σw = 0:01 with blind channel estimation.
show the efficacy of such methods even in blind case when the channel is unknown. Furthermore, we note that other techniques such as the Particle Filter method can be used to improve its effectiveness further.