Successive interference cancellation multi-user-detector

The approach we used so far can be categorized as a single-user estimation approach, basically treating the undesired user signals as interference. Another class of solutions is the multiuser estimation approach that tries to estimate all CFOs either sequentially or simultaneously. We suggest a simple approach similar to the so-called successive interference cancellation used in multiuser detection theory. In accordance with con-ventional terminology, we refer to our solution as a Successive Interference Cancellation Multi-User-Estimate (SIC MUE), which can be briefly summarized as follows.

The SIC MUE Algorithm

Assuming that there are L > m users whose power can be estimated and the data sequences of the m strongest received sequence has been successfully detected.

1. Order the received signal according to their estimated power.

2. Regenerate a copy of the sum of the strongest received m signals.

3. Subtract from the received signal the regenerated copy to obtain a partially-interference-suppressed version of the received sequence.

4. Perform joint CFO and channel estimate on the strongest signal among the re-maining L − m ones based on sequence obtained in the previous step.

5. Make a hard decision on this strongest signal after compensating for the CFO and channel distortion.

6. m ← m + 1, if m ≤ L go to Step 2; otherwise output all the detected sequences and stop.

. . . Remove FFT

ADC CP .

. .

S/P .

. .

CFO Estimation

Channel Estimation

Received signal

Detected information

symbols

... ...

Tentative Data Decision Estimated

Channel Info Estimated

CFO Info

Regenerate an estimate of received signal of the user

Figure 3.16: An SIC Multiuser detector with iterative CFO and channel estimates.

. . .

Received siganl of user 0 Received siganl of user 1

Received siganl of user K-1 AWGN

   

   

Received siganl at base station

Regenerate an estimate of received signal of strongest user

   

A partially cleaned version of the received siganl at base station

Figure 3.17: An illustration of the SIC MUE concept.

.

Regenerate estimate of received signal of the users

Remove CP

Figure 3.18: Steps 1 - 3 of the SIC MUE algorithm.

.

Regenerate an estimate of received signal of the user

Remove CP

Figure 3.19: Steps 4 of the SIC MUE algorithm.

.

Regenerate an estimate of received signal of the user

Remove CP

Figure 3.20: Steps 5 and 6 of the SIC MUE algorithm.

Chapter 4 Conclusions

We have extended both Moose’s and Yu’s maximum likelihood CFO estimation algorithms for use in MIMO-OFDM systems. As long as the length of cyclic prefix is greater than or equal to the maximum delay that accounts for the all users’ timing ambiguities and channel multipath delays. The performance of both CFO estimates improves as the number of transmit/receive antennas increases. In other words, the presence of multiple antenna not only promise great capacity enhancement but entail performance improvement for the associated frequency synchronization subsystem.

The frequency synchronization problem for asynchronous OFDM-CDMA systems in frequency selective fading channels is also studied. Assuming frame synchronization has been established and channel estimation is available, we employ a constrained minimum output energy (MOE) criterion for estimating the CFO. The estimate is obtained by searching for the value that yields the largest MOE. Following an approach similar to Yu’s ML algorithm, we convert the min/max search into a polynomial rooting problem.

The range of the proposed estimate is ±^N₂ subcarrier spacings where N is the frame (DFT) size. Performance depends mainly on multiuser interference, thermal noise and number of pilot blocks.

When no channel information is available, the proposed CFO estimate deteriorates and thus some joint frequency and channel estimation scheme is urgently needed. Iter-ative solutions are suggested but have not been proved. Besides, the proposed solutions

belong to the class of blind estimates that require a relatively long convergence period. It would be very much welcome if we can find either an improved–less complicated and/or faster convergence rate–blind estimate or a pilot-assisted solution with little or no learn-ing period. A joint timlearn-ing-frequency-channel estimation algorithm for OFDM-CDMA systems is called for as well.

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