Advanced Antenna System in Network Deployments

Asplund, Henrik; Astély, David; Butovitsch, Peter von; Chapman, Thomas; Frenne, Mattias; Ghasemzadeh, Farshid; Hagström, Måns; Hogan, Billy; Jöngren, George; Karlsson, Jonas; Kronestedt, Fredric; Larsson, Elisabeth

doi:10.1016/b978-0-12-820046-9.00014-9

Cited by 21 publications

(30 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Radio channel describes the relation between the transmitted and received signals. Radio channel model comprises two parts: physical channel that captures physical wave propagation between transmitter and receiver and antenna array model that captures antenna configuration at both link ends [4]. In other words, physical channel focuses on wave propagation between the location of transmitter and receiver without taking the antenna array into account while antenna array model captures the impact of transmitting and receiving antenna through antenna radio patterns.…”

Section: A Channel Modelmentioning

confidence: 99%

Deep Learning for Wireless Dynamics

Lee¹,

Jeong²,

Wang³

2022

Preprint

View full text Add to dashboard Cite

This paper aims to predict radio channel variations over time by deep learning from channel observations without knowledge of the underlying channel dynamics. In nextgeneration wideband cellular systems, multicarrier transmission for higher data rate leads to high-resolution predicting problem. By leveraging recent advances of deep learning in high-resolution image processing, we propose a purely data-driven deep learning (DL) approach to predicting high-resolution temporal evolution of wideband radio channels. In order to investigate the effect of architectural design choices, we develop and study three deep learning prediction models, namely, baseline, image completion and next-frame prediction models using UNet. Numerical results show that the proposed DL approach achieves a 52% lower prediction error than the traditional approach based on Kalman filter (KF) in mean absolute errors. To quantify impact of channel aging and prediction on precoding performance, we also evaluate the performance degradation due to outdated and predicted channel state information (CSI) compared to perfect CSI. Our simulations show that the proposed DL approach can reduce the performance loss due to channel aging by 71% through adapting precoding vector to changes in radio channel while the traditional KF approach only shows a 27% reduction.

show abstract

Section: A Channel Modelmentioning

confidence: 99%

Deep Learning for Wireless Dynamics

Lee¹,

Jeong²,

Wang³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Since an IRS is a passive device, we introduce a normalization factor of 1 √ 2 . As one can observe in (2), by performing polarization beam splitting, the IRS was capable of delivering two replicas with independent phases and amplitudes of the transmitted data symbol.…”

Section: Fundamentals Of a Dual-polarized Irsmentioning

confidence: 99%

“…Observe that, the channel in (5) consists of a generalization of that introduced in Section II, with the difference that now both the transmitter, i.e., the BS, and receivers employ multiple dual-polarized antennas. Also, notice that we model depolarization phenomena in the links BS-U and BS-IRS, but not in the link IRS-U 2 . This means that only negligible power leaks between polarizations in the propagation channels between the IRSs and users.…”

Section: System Modelmentioning

confidence: 99%

“…In addition, since antennas with orthogonal polarizations exhibit a low correlation, dual-polarization enables massive MIMO-NOMA systems to exploit diversity in the polarization domain, which can significantly outperform conventional single-polarized schemes [1]. Due to these attractive features, dual-polarized antenna arrays have been adopted as standard in the 3rd generation partnership project (3GPP) long-term evolution advanced (LTE-A) and 5G New Radio (NR) specifications [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization

Sena¹,

Nardelli²,

Costa³

et al. 2020

Preprint

View full text Add to dashboard Cite

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain.Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratic constrained quadratic sub-problems, which makes it possible to obtain the optimal solution via interior-points methods. We also derive analytically a closed-form expression for the users' ergodic rates by considering large numbers of reflecting elements. This is followed by representative simulation examples and comprehensive discussions. The results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dualpolarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through diversity.

show abstract

“…Dual-polarized antenna arrays are effective for overcoming physical space limitations in multiple-input multiple-output (MIMO) systems [1]. By organizing dual-polarized antennas into co-located pairs, one can double the number of antennas in arrays with the same dimensions as those used in single-polarized schemes.…”

Section: Introductionmentioning

confidence: 99%

Dual-Polarized IRSs in Uplink MIMO-NOMA Networks: An Interference Mitigation Approach

Sena

Nardelli

Costa

et al. 2021

IEEE Wireless Commun. Lett.

View full text Add to dashboard Cite

In this work, intelligent reflecting surfaces (IRSs) are optimized to manipulate signal polarization and improve the uplink performance of a dual-polarized multiple-input multipleoutput (MIMO) non-orthogonal multiple access (NOMA) network. By multiplexing subsets of users in the polarization domain, we propose a strategy for reducing the interference load observed in the successive interference cancellation (SIC) process. To this end, dual-polarized IRSs are programmed to mitigate interference impinging at the base station (BS) in unsigned polarizations, in which the optimal set of reflecting coefficients are obtained via conditional gradient method. We also develop an adaptive power allocation strategy to guarantee rate fairness within each subset, in which the optimal power coefficients are obtained via a low-complexity alternate approach. Our results show that all users can reach high data rates with the proposed scheme, substantially outperforming conventional systems.

show abstract

Advanced Antenna System in Network Deployments

Cited by 21 publications

References 0 publications

Deep Learning for Wireless Dynamics

Deep Learning for Wireless Dynamics

IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization

Dual-Polarized IRSs in Uplink MIMO-NOMA Networks: An Interference Mitigation Approach

Contact Info

Product

Resources

About