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Swarm Intelligent Based Detection in the Uplink of Large-Scale MIMO Wireless Communication Systems

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Design Frameworks for Wireless Networks

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 82))

Abstract

Large-scale multiple-input multiple-output (MIMO) system plays a vital role in realizing the ever-increasing demand for high-speed data in 5G and beyond wireless communication systems. MIMO systems employ multiple antennas at both the transmitter and receiver. These systems can achieve both the spatial diversity and the spatial multiplexing gain, which are required for enhancing the quality of service (QoS) and the capacity of wireless systems, respectively. Howbeit, reliable detection of the transmitted data streams is challenging due to the presence of inter-channel interference and inter-user interference. To address the above symbol detection issues, maximum likelihood (ML) (Van Trees, Detection, estimation, and modulation theory, part I: detection, estimation, and linear modulation theory, 2004, [34]) detection performs an exhaustive search over all the possible transmitted information symbols and achieves optimal bit error rate (BER) performance. However, being an NP-Hard problem, ML detection is practically unfeasible for large MIMO systems. Therefore, alternate low-complexity robust detection techniques are being devised for near-optimal detection in large MIMO systems. Nature-inspired algorithms have been an emerging choice to obtain a better solution for combinatorial optimization problems. Recently, nature-inspired algorithms has attracted the attention of researchers from wireless communication community, due to its simple implementation and low-complexity behaviour in solving research problems in communication. In this chapter, we have discussed some of the promising bio-inspired techniques such as ant colony optimization and social spider optimization, and introduced one of the key applications of these algorithms, that is, to solve the combinatorial optimization problem of symbol detection in large-scale MIMO systems. We have also compared the BER performance of different bio-inspired algorithms with the traditional low-complexity detection techniques such as zero forcing and minimum mean squared error detectors.

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Acknowledgements

This chapter is an outcome of Research and Development work undertaken project under Ministry of Electronics and Information Technology, being implemented as Digital India Corporation. The authors would also like to thank Indian Institute of Technology Indore for all the support.

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Authors and Affiliations

  1. Discipline of Electrical Engineering, IIT Indore, Indore, Madhya Pradesh, India

    Arijit Datta & Vimal Bhatia

  2. Department of Electronics and Telecommunications Engineering, SKVM’s NMIMS (Deemed to be University), Shirpur Campus, Shirpur, 425405, Maharashtra, India

    Manish Mandloi

Authors
  1. Arijit Datta
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  2. Manish Mandloi
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  3. Vimal Bhatia
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Corresponding author

Correspondence to Arijit Datta .

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Editors and Affiliations

  1. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Brahmapur, Odisha, India

    Santosh Kumar Das

  2. Department of Computer Science and Engineering, University Institute of Technology, University of Burdwan, Bardhaman, West Bengal, India

    Sourav Samanta

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

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Datta, A., Mandloi, M., Bhatia, V. (2020). Swarm Intelligent Based Detection in the Uplink of Large-Scale MIMO Wireless Communication Systems. In: Das, S., Samanta, S., Dey, N., Kumar, R. (eds) Design Frameworks for Wireless Networks. Lecture Notes in Networks and Systems, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-13-9574-1_13

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  • DOI: https://doi.org/10.1007/978-981-13-9574-1_13

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