Adaptive Subcarrier Grouping for MMSE-SMI Adaptive Array Interference Suppression

Abstract

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

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

“…Figure 10 and 11 show the BER performance comparison between the conventional SMI, CCM-SMI, adaptive subcarrier grouping [19], the proposed adaptive zero-padding. The effect of the conventional and proposed methods is evaluated with different LoS components.…”

“…In the above scheme, the effect of noise has been not fully considered when calculating the array weight. By exploiting adaptive grouped subcarriers with correlated frequency responses for averaging, the convergence precision of the covariance matrix can be improved [19]. However, in order to suppress interference, how to choose subcarriers for grouping is very necessary.…”

“…However, if these algorithms are applied, it will increase the complexity of communication. The paper [19] does not consider the computational problem and estimation accuracy. In addition, adaptive subcarrier grouping also eliminates the noise of the received signal matrix 𝑿 through the noise reduction averaging algorithm.…”

“…To solve the above problem, an adaptive subcarrier grouping (ASG) method has been proposed [19]. In ASG method, the standard deviations among the adjacent subcarriers are calculated, averaged, and compared with the threshold to determine the grouping.…”

Data-aided Weight with Subcarrier Grouping for Adaptive Array Interference Suppression

“…For this reason, it cannot work in the heterogeneous deployment of small cells. A more recent work evaluates the effect under the above situation [15]. Reference [15] proposed an adaptive subcarrier grouping (ASG) method.…”