Dual QR decomposition in K-best sphere detection for Internet of Things networks

Internet of Things (IoT) in 5G communications is becoming more popular due to its robust features. Multiple input multiple output (MIMO) in IoT is playing a vital role nowadays with large and massive concepts. At the same time detection in such scenario is becoming complex day by day. Sphere decodin...

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Bibliographic Details
Published in:Cluster computing 2019-07, Vol.22 (Suppl 4), p.7713-7722
Main Authors: Syed Moinuddin Bokhari, B., Bhagyaveni, M. A.
Format: Article
Language:English
Subjects:
Algorithms
Bit error rate
Complexity
Computer Communication Networks
Computer Science
Decomposition
Internet of Things
MIMO communication
Operating Systems
Processor Architectures
Random noise
Receivers & amplifiers
Transmitters
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Summary:Internet of Things (IoT) in 5G communications is becoming more popular due to its robust features. Multiple input multiple output (MIMO) in IoT is playing a vital role nowadays with large and massive concepts. At the same time detection in such scenario is becoming complex day by day. Sphere decoding algorithms is one of the MIMO detection schemes used for achieving near-optimal maximum likelihood detection (MLD) performance. Preprocessing algorithms such as lattice reduction and QR-decomposition (QRD) has been widely used along with the variants of MIMO detection for achieving better bit error rate (BER) performance. This research paper proposes a novel dual QRD (DQRD) algorithm to obtain a universal low complex-lattice reduction aided (UL-LRA)—K-best sphere detection (KSD) for MIMO networks. The proposed algorithm is evaluated under additive white Gaussian noise flat fading and indoor task group ac (TGac-channel D) channel model. The results proved that the BER performance was enhanced as well as the average runtime complexity of LRA–KSD was reduced by 45 and 39% in a flat faded and channel D respectively compared to the optimal MLD using 16-QAM.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-1307-4