OMP with Grid-Less Refinement Steps for Compressive mmWave MIMO Channel Estimation

Weiland, Lorenz; Stockle, Christoph; Wurth, Michael; Weinberger, Thomas; Utschick, Wolfgang

doi:10.1109/sam.2018.8448789

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Hybrid precoding with multiple numbers of antennas can also provide CSI; however, channel subspace sampling constraints affect its performance in digital baseband systems and also limit complete channel information [20]. Orthogonal matching pursuit (OMP) [21][22][23][24][25] exploits sparse properties of mmWaves which is a promising technique but it does not perform well due to its complicated nature if the number of UEs is high or in case of higher number of handovers.…”

Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

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Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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“…Another promising technique in the literature is orthogonal matching pursuit (OMP), used by [18][19][20][21][22] which exploit the sparse properties of mm waves. Authors in [20] argued that the channel can be estimated using a parametric model having quantized AoDs and AoAs.…”

Section: Literature Reviewmentioning

confidence: 99%

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

2020

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In a network topology, where 5G (mm Waves) have better coverage footprint compared to 4G (LTE or LTE-A) technology, mobile devices would generally be handed over from 4G to 5G. In this work, a supervised intelligent prediction technique for improved handover success rate (HSR) from 4G to 5G technology is proposed. The technique is applicable for base stations enabled with sub-6-GHz and mm-wave bands. This technique is novel since it can predict HSR even before switching to 5G radio circuitry or initiating its measurement gap for acquisition of mm-wave reference signal received power (RSRP) unlike conventional algorithms. Thus, preempting all handovers which are likely to fail will provide improvements in latency, delay, and handover success rate, as well as decrease call drops. Therefore, this research work answers previous research shortcomings and can unleash applications of supervised intelligent algorithms for predicting the HSR from 4G to 5G. The proposed algorithm is validated by showing improvements obtained through simulation results performed using Python-based framework. The proposed algorithm is tested for reliability with increasing parameters such as the intensity number of UEs and simulation time. Improvements in standard handover algorithm are also proposed.

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mentioning

confidence: 99%

“…Due to the sparsity of millimeter-wave channel, compressed sensing technology naturally becomes a channel estimation method [19][20][21][22][23]. For mmWave massive MIMO systems, the authors in [19,20] proposed the OMP algorithm for signal recovery based on compressed sensing. The work of [21] proposed an efficient CS channel estimation method, which is based on time-domain and frequency-domain methods [22,23].…”

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confidence: 99%

Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

Zhang

Yang

Tang

et al. 2023

IEEE Open J. Commun. Soc.

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Millimeter wave (mmWave) communications are widely preferred due to the rich bandwidth and potentially huge spectrum resources. Nowadays, mixed-ADC architecture combined with mmWave massive MIMO has become a communication mainstream, which can effectively solve the issue of high total power consumption and cost of base station (BS) circuits. However, the channel estimation problem for mmWave massive MIMO systems with mixed-ADC architecture has not been studied yet. In this paper, we develop the sparse channel estimation method on this framework. Specifically, by exploiting the sparsity of mmWave channels, the beamspace channel estimation problem can be transformed into a sparse matrix recovery problem, the channel parameters are recovered using compressive sensing (CS) techniques. Simulation results show that the algorithms quantized by the mixed-ADC outperforms the low-resolution ADC, and the best performance can be achieved when the low-resolution ADC in the mixed-ADC architecture reaches five-bit.

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OMP with Grid-Less Refinement Steps for Compressive mmWave MIMO Channel Estimation

Cited by 4 publications

References 20 publications

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

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