Data-aided SMI Algorithm using Common Correlation Matrix for Adaptive Array Interference Suppression

Shima, Kosuke; Senoo, Katsuya; Goto, Kenta; Maruta, Kazuki; Ahn, Chang-Jun

doi:10.1109/apcc47188.2019.9026456

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it still has a problem of inaccurate channel estimation. As stated above, its applicable channel environment is limited and hence it is incompatible with various communication scenarios [18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Zero-Padding with Impulsive Training Signal MMSE-SMI Adaptive Array Interference Suppression

Kojima

Maruta

et al. 2023

IEICE Trans. Fundamentals

Self Cite

View full text Add to dashboard Cite

In mobile communication systems, the channel state information (CSI) is severely affected by the noise effect of the receiver. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. Although it can reduce the additive noise effect by increasing samples to derive the array weight for co-channel interference suppression, it needs to know the signal-to-noise ratio (SNR) in advance to set the threshold for subcarrier grouping. This paper newly proposes adaptive zero padding (AZP) in the time domain to improve the weight accuracy of the SMI matrix. This method does not need to estimate the SNR in advance, and even if the threshold is always constant, it can adaptively identify the position of zero-padding to eliminate the noise interference of the received signal. Simulation results reveal that the proposed method can achieve superior bit error rate (BER) performance under various Rician K factors.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Zero-Padding with Impulsive Training Signal MMSE-SMI Adaptive Array Interference Suppression

Kojima

Maruta

et al. 2023

IEICE Trans. Fundamentals

Self Cite

View full text Add to dashboard Cite

show abstract

“…Exploiting the data part is also valid to compensate for the above drawback. We previously appended a decision feedback approach further to reduce the required data symbol amounts [15]. Decision results of received symbols are obtained by initial estimation and then fed-back to the channel estimator.…”

mentioning

confidence: 99%

“…It can contribute to improving interference suppression capability even with the limited number of symbols available. In contrast to [14] and [15], whose availability is limited to frequency flat channels, the effective region of our proposal is generalized; it well works in almost all fading environments, especially in loose frequency selectivity. As expected, our proposed scheme attains outstanding bit error rate (BER) performance at large K, which provides wide coherence bandwidth.…”

mentioning

confidence: 99%

Adaptive Subcarrier Grouping for MMSE-SMI Adaptive Array Interference Suppression

et al. 2021

Self Cite

View full text Add to dashboard Cite

Inter-user interference (IUI) suppression is a prime matter for mobile communication systems. Channel state information (CSI), meanwhile, is severely affected by the receiver additive noise effect. Sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array is a powerful means to suppress noise enhancement with the aid of data symbols. However, its effectiveness is limited under an insufficient number of symbols available. Based on the multi-carrier transmission system, this paper proposes an adaptive subcarrier grouping to expand the number of received symbols for a covariance matrix calculation. The grouping size is adaptively adjusted by observing the dispersion of adjacent subcarriers. Simulative results reveal the effectiveness of the proposed adaptive algorithm; superior bit error rate (BER) performance can be attained under various Rician K factors. INDEX TERMS Adaptive array, Interference suppression, Channel estimation, Sample matrix inversion, Subcarrier grouping, Rician fading I. INTRODUCTION M OBILE data traffic is continuously increasing due to widespread mobile terminals such as smartphones or tablets. Besides, machine to machine (M2M) communications will induce further explosion of wireless connectivity. Global Internet Protocol (IP) traffic is expected to grow more than triple from 2017 to 2022 , and 14.6 billion M2M devices, which accounts for half of all devices, will be connected to the network in 2022 [1]. The fifth-generation (5G) [2] mobile communications system and beyond are promising technologies to support these enormous traffic and connections increase. They require the realization of huge capacity, outstanding throughput, and high dense connectivity. Key solutions for these demands are massive multiple-input multiple-output (MIMO) system and usage of millimeterwave (mmWave) [3], [4]. The former can be utilized in various means such as beamforming [5], suppressing unknown interference from other cells and inter-user interference (IUI) [6], and mobility resistance [7]. Its high directivity also heightens the connection density. Although mmWave contributes to enhancing throughput

show abstract