Minimum Mean-Squared Error Iterative Successive Parallel Arbitrated Decision Feedback Detectors for DS-CDMA Systems
read more
Citations
Adaptive power allocation strategies for distributed space-time coding in cooperative MIMO networks
Study of Robust Two-Stage Reduced-Dimension Sparsity-Aware STAP with Coprime Arrays.
Study of Sparsity-Aware Reduced-Dimension Beam-Doppler Space-Time Adaptive Processing
Multibeam joint detection
Study of Channel Estimation with Oversampling for 1-bit Large-Scale MIMO Systems.
References
Digital communications
Multiuser Detection
Minimum probability of error for asynchronous Gaussian multiple-access channels
Iterative (turbo) soft interference cancellation and decoding for coded CDMA
Linear multiuser detectors for synchronous code-division multiple-access channels
Related Papers (5)
Multiple Feedback Successive Interference Cancellation Detection for Multiuser MIMO Systems
Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering
Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters
Frequently Asked Questions (17)
Q2. What is the motivation for the proposed encoded structure?
The motivation for the proposed encoded structure is that significant gains can be obtained from iterative techniques with soft cancellation methods and error control coding [17]-[23] and from efficient receivers structures and ordering algorithms such as the novel SPA-DF detector.
Q3. What is the role of reversing the cancellation order in successive stages?
The role of reversing the cancellation order in successive stages is to equalize the performance of the users over the population or at least reduce the performance disparities.
Q4. What is the feedback filter fk(i) of user k?
In particular, the feedback filter fk(i) of user k has a number of non-zero coefficients corresponding to the available number of feedback connections for each type of cancellation structure.
Q5. How many users can be supported in comparison with the ISP-DF?
The ISPAP-DF scheme can save up to 1.4 dB and support up to 8 more users in comparison with the ISP-DF for the same BER performance.
Q6. How many additional users can be supported in comparison with the ISP-DF?
the ISPASPA-DF detector can save up to 1.8 dB and support up to 10 additional users in comparison with the ISP-DF for the same BER performance.
Q7. What is the motivation for the proposed iterative receiver structure?
2. The proposed iterative (turbo) receiver structure consists of the following stages: a soft-input-soft-output (SISO) SPA-DF detector and a maximum a posteriori (MAP) decoder.
Q8. What is the reason why the linear and P-DF detectors experience performance losses for coded?
It is worth noting that the linear and P-DF detectors experience performance losses for coded systems, relative to the other structures, as verified in [14] and which is a result of the loss in spreading gain that increases the interference power at the output of the MMSE receiver.
Q9. What is the SPA-DF receiver with hard-decision feedback?
An iterative receiver with hard-decision feedback is defined by:z(m+1)(i) = WH(i)r(i) − FH(i)b̂(m)(i) (23)where the filters W and F can be S-DF or P-DF structures, and b̂m(i) is the vector of tentative decisions from the preceding iteration that is described by:b̂(1)(i) = sgn ( ℜ [ WH(i)r(i) ])(24)b̂(m)(i) = sgn ( ℜ [ z(m)(i) ]) , m > 1 (25)where the number of stages m depends on the application.
Q10. What is the a posteriori LLR of the code bit?
The MAP decoder also computes the a posteriori LLR of every information bit, which is used to make a decision on the decoded bit at the last iteration.
Q11. What is the motivation for the proposed iterative SPA-DF receiver?
Iterative Turbo Receiver and DecodingA CDMA system with convolutional codes being used at the transmitter and the proposed iterative SPA-DF receiver with turbo decoding is illustrated in Fig.
Q12. Why do the authors adopt L = 4 for the remaining experiments?
For this reason, the authors adopt L = 4 for the remaining experiments because it presents a very attractive trade-off between performance and complexity.
Q13. What are the disadvantages of S-DF relative to PDF?
From the curves, the authors observe that a disadvantage of S-DF relative to PDF is that it does not provide uniform performance over the user population.
Q14. What is the motivation for the proposed iterative detection schemes?
The decoding of the proposed iterative detection schemes that employ the SPA-DF detector (ISPAS-DF, ISPAP-DF and ISPASPA-DF) resembles the uncoded case, where the second stage benefits from the enhanced estimates provided by the first stage that now employs convolutional codes followed by a Viterbi decoder with branch metrics based on the Hamming distance.
Q15. What is the a posteriori probability of the detector?
These estimates are used to compute the detector a posteriori probabilities P [bk(i) = ±1|z(m)k (i)] which are deinterleaved and input to the MAP decoder for the convolutionalcode.
Q16. What is the disadvantage of the SPA-DF receiver?
As occurs with S-DF receivers, a disadvantage of the SPA-DF detector is that it generally does not provide uniform performance over the user population.
Q17. What is the main feature of the proposed detectors?
This is an important feature of the proposed detectors as they can save considerable computational resources by operating with a lower number of turbo iterations.