Element-based Lattice Reduction aided K-Best detector for large-scale MIMO systems

Recently, large-scale Multiple-Input Multiple-Output (MIMO) systems have caught great attention for increasing the system throughput as well as improving the system performance. The main challenge in the design of these MIMO systems is the detection techniques used at the receiver. Lattice Reduction (LR) techniques have shown good potential in MIMO decoding due to their good performance and low complexity compared to Maximum Likelihood (ML) detector. The Lenstra, Lanstra, and Lovasz (LLL) LR algorithm has been employed for decoding while combined with linear detectors such as ZF as well as with K-Best detection. However, the LLL-aided detectors have shown limited performance, when increasing the number of antennas at the transmitter and receiver. Therefore, in this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better BER performance and lower complexity than the LLL-aided detection. Explicitly, the ELR-aided detectors are capable of attaining a 2 dB performance improvement at BER of 10-5 compared to the LLL-aided detectors when considering a MIMO system with 200 transmit and receive antennas. Furthermore, for the same MIMO configuration, the ELR basis update requires nearly an order of magnitude reduction in the number of arithmetic operations compared to the LLL algorithm

Traffic Learning: A Deep Learning Approach for Obtaining Accurate Statistical Information of the Channel Traffic in Spectrum Sharing Systems

In recent works, the statistical information of the channel traffic has been increasingly exploited to make effective decisions in spectrum sharing systems. However, these statistics cannot be obtained perfectly under (realistic) Imperfect Spectrum Sensing (ISS). Therefore, in this work we study comprehensively the approaches in the literature that correct the estimation of the channel traffic statistics under ISS, namely the closed-form expression approach and the algorithmic reconstruction approach. Then, we introduce a novel approach named Traffic Learning as a Deep Learning (DL) approach for providing accurate estimation of the channel traffic statistics under ISS. For this novel approach, deep neural networks using Multilayer Perceptron (MLP) models are found for the estimation of several statistical metrics. In addition, we show that utilising effective features from spectrum sensing observations can lead to a considerable improvement in statistics estimation for each, mean, variance, minimum and distribution of the channel traffic under ISS, outperforming the existing approaches in the literature, which are based on either closed-form expressions or reconstruction algorithms

Energy-efficient hardware implementation of LR-aided K-Best MIMO decoder for 5G networks

Energy efficiency is a primary design goal for future green wireless communication technologies. Multiple-input multiple-output (MIMO) schemes have been proposed in literature to improve the throughput of communication systems, they are expected to play a prominent role in the upcoming 5th generation (5G) standard. This paper presents a novel high efficiency MIMO decoder based on the K-Best algorithm with lattice reduction. We have designed a novel hardware architecture for this decoder, which was implemented using 32nm standard CMOS technology. Our results show that the proposed decoder can achieve on average a four-fold reduction in the power costs compared to recently published designs for 5G networks. The throughput of the design is 506 Mbits/sec, which is comparable to existing designs