Abstract

Spatial-multiplexing multiple-input multiple-output (MIMO) systems have been developed and enhanced over the past two decades. In particular, a great amount of effort has gone towards development of capacity achieving detectors with affordable computational complexity. The developed detectors may be broadly divided into two classes: (i) deterministic sampling, such as list sphere decoding detector; and (ii) stocastic sampling, such as those based on Markov chain Monte Carlo (MCMC) search schemes. This paper proposes a novel detection scheme that is based on stochastic sampling, but is fundamentally different from the MCMC detectors. While MCMC follows a set of sequential sampling steps, hence, the sample sets obtained are highly correlated, the method proposed in this paper takes stochastic samples that are completely independent. This new approach of stochastic sampling leads to a detector with significantly reduced complexity. It also allows reduction in the detector latency.

Highlights

  • The use of spatial-multiplexing in multiple-input multipleoutput (MIMO) communication systems is an effective method for increasing spectral efficiency, by using spatial diversity to send multiple data symbols over the same spectrum

  • Codeword error rate (CER) is used instead of bit error rate (BER) because LDPC can fail catastrophically which can lead to irregular BER curves

  • Using CER is more consistent with real-world performance metrics which focus on block and packet error rate performance, since any number of errors will result in a full failure of the block or possibly packet

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Summary

A Capacity Achieving MIMO Detector Based on Stochastic Sampling

This paper proposes a novel detection scheme that is based on stochastic sampling, but is fundamentally different from the MCMC detectors. While MCMC follows a set of sequential sampling steps, the sample sets obtained are highly correlated, the method proposed in this paper takes stochastic samples that are completely independent. This new approach of stochastic sampling leads to a detector with significantly reduced complexity. It allows reduction in the detector latency

INTRODUCTION
SYSTEM MODEL
PROPOSED STOCHASTIC DETECTOR
STOCHASTIC SAMPLING METHODS
Independent and dependent samples
Limiting the sampling radius
COMPLEXITY ANALYSIS
SIMULATION RESULTS
STLD-ZF versus STLD-MMSE detector
Output LLR Moderation
Dependent and independent samples
CONCLUSION

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