Spatial Domain Management and Massive MIMO Coordination in 5G SDN

Sun, Songlin; Rong, Bo; Hu, Rose Qingyang; Qian, Yi

doi:10.1109/access.2015.2498609

Cited by 16 publications

(12 citation statements)

References 66 publications

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“…For user-u, the downlink CSI (i.e., H u ) is spread firstly. Then the weighted downlink CSI and UL-US are superimposed together to form signal X u , as given in (1). Over the attenuation of the uplink channel G u and link noise N u , the transmitted X u from user-u is received at BS.…”

Section: B Learning Task For Sc-based Csi Feedbackmentioning

confidence: 99%

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Deep Learning for CSI Feedback Based on Superimposed Coding

et al. 2019

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Massive multiple-input multiple-output (MIMO) with frequency division duplex (FDD) mode is a promising approach to increasing system capacity and link robustness for the fifth generation (5G) wireless cellular systems. The premise of these advantages is the accurate downlink channel state information (CSI) fed back from user equipment. However, conventional feedback methods have difficulties in reducing feedback overhead due to significant amount of base station (BS) antennas in massive MIMO systems. Recently, deep learning (DL)-based CSI feedback conquers many difficulties, yet still shows insufficiency to decrease the occupation of uplink bandwidth resources. In this paper, to solve this issue, we combine DL and superimposed coding (SC) for CSI feedback, in which the downlink CSI is spread and then superimposed on uplink user data sequences (UL-US) toward the BS. Then, a multi-task neural network (NN) architecture is proposed at BS to recover the downlink CSI and UL-US by unfolding two iterations of the minimum mean-squared error (MMSE) criterion-based interference reduction. In addition, for a network training, a subnet-by-subnet approach is exploited to facilitate the parameter tuning and expedite the convergence rate. Compared with standalone SC-based CSI scheme, our multi-task NN, trained in a specific signal-to-noise ratio (SNR) and power proportional coefficient (PPC), consistently improves the estimation of downlink CSI with similar or better UL-US detection under SNR and PPC varying. INDEX TERMS Channel state information (CSI), deep learning (DL), superimposed coding (SC), feedback, massive multiple-input multiple-output (MIMO).

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Section: B Learning Task For Sc-based Csi Feedbackmentioning

confidence: 99%

“…As one of the key technologies in the fifth generation (5G) wireless communication system, massive multipleinput multiple-output (MIMO) has now motivated a growing research interest [1]. In massive MIMO systems, hundreds of antenna elements are deployed at the base station (BS).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for CSI Feedback Based on Superimposed Coding

et al. 2019

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“…Motivated by these considerations, it is encouraged to concentrate on the development of an efficient MU precoding scheme based PAPR reduction that aims at performing jointly the MU precoding and the PAPR reduction without sacrificing neither the spectral efficiency nor the transmission quality nor the spectral purity. It should exploit the extra null-space of the associated massive MIMO channel [13]. A big emphasis should be put on reducing the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

PAPR-Aware Massive MIMO-OFDM Downlink

2019

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We investigate the peak-to-average power ratio (PAPR) reduction problem in orthogonal frequency-division multiplexing-based massive multiuser (MU) multiple-input multiple-output (MIMO) downlink systems. In this paper, we develop a downlink transmission scheme that performs jointly MU precoding and PAPR reduction (PP) by exploiting the excess degrees of freedom offered by equipping the BS by a large number of antennas. Specifically, the joint MU precoding and PAPR reduction scheme is formulated as a simple convex optimization problem solved via steepest gradient descent (GD) approach. Then, we develop a novel algorithm, referred to as MU-PP-GDm, to reduce the PAPR of the transmitted signals by exploiting the high-dimensional null space of the MIMO channel matrix while maintaining an excellent transmission quality. The simulation results show that the proposed MU-PP-GDm has low computational complexity and can achieve substantial PAPR performance with a fast convergence rate. INDEX TERMS 5G+, massive multiple-input multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), peak-to-average power ratio (PAPR), multiuser (MU) precoding, convex optimization, gradient descent (GD). The associate editor coordinating the review of this manuscript and approving it for publication was Jiayi Zhang. In practice, broadband wireless communications, however, encounter large delay spread, and, therefore, suffer from frequency-selective fading. Orthogonal frequency-division multiplexing (OFDM), a scheme of encoding digital symbols on multiple orthogonal sub-carriers, is an efficient and well-established way to deal with frequency-selective channels. Therefore, massive MIMO-OFDM is a very promising combination to meet the ever growing demands for higher link readability and spectrum efficiency of next-generation wireless communication systems (5G and Beyond). However, massive MIMO precoders exhibit transmit signals with high peak-to-average power ratio (PAPR), regardless of whether single-carrier or OFDM transmission and of whether a low or a high modulation order is used [4]. Consequently, the nonlinearity of the radio frequency (RF) high-power amplifier (HPA), which is necessary in a transmission chain, yields in-band distortion and out-of-band radiation (OBR) which cause signal distortion, phase rotation and adjacent channel interference, respectively. To avoid such heavy distortions, the transmit signals require that the power amplifiers are backed-off and operated in their linear region (i.e., where their transfer characteristics are sufficiently linear). Nevertheless, operating at lower power levels reduces 25474 2169-3536 2019 IEEE. Translations and content mining are permitted for academic research only.

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“…WSDN provides an alternative for dynamic spatial domain radio resource processing [96]. Traditional BS can only collect partial channel state information due to the distance limitation, whereas the interference from other cells are normally neglected, which can be problematic.…”

Section: Wsdn-based Massive Mimo Coordination Schemementioning

confidence: 99%