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ADvanced multicarrier wAveforms based Massive MIMO with full-duplex capability for green 5G and beyond

Periodic Reporting for period 1 - ADAM5 (ADvanced multicarrier wAveforms based Massive MIMO with full-duplex capability for green 5G and beyond)

Reporting period: 2018-11-01 to 2020-10-31

ADAM5 aims to advance the development of the composite of two concepts: Massive MU-MIMO and post-OFDM Multicarrier Waveforms (MWF), a new and most promising direction in 5G & beyond. ADAM5 will substantially contribute to the development of practical solutions to enable highly spectral-efficient, very low-cost and highly energy-efficient 5G systems. ADAM5 target is to propose new digital signal processing based solutions coping with the expected 5G requirements, with a particular focus on mitigating RF impairments and reducing energy consumption of wireless devices, through a set of new algorithms and advanced techniques.
The goals of the ADAM5 are to:
1) Analyze the effect of the most essential RF impairments on the quality of future wireless communications exploiting 5G MWFs based Massive MIMO in terms of BER and PSD.
2) Develop reliable digital signal processing based solutions to mitigate RF impairments on both the transmitter and receiver sides of 5G communication systems. This will enable extensive use of low-cost and low-power components (e.g. mMTC devices in IoT), reduced latency, flexibility to accommodate various services, simplification of the multiple access, and robustness to interference.
3) Drastically reduce the emitted RF power so that the total energy consumption of a mobile network is lowered when implemented with simple, low-power hardware. Joint optimization for MWF design and Massive MIMO precoding technique will be studied.
4) Introduce new efficient joint approach for PAPR reduction and PA linearization adapted to the more pertinent 5G MWF based Massive MIMO. Trade-off between efficiency and linearity will be theoretically analyzed.
In this report, I expose the main researches I conducted, within the H2020 MSCA ADAM5 project, to tackle the aforementioned massive MU-MIMO issues. It is divided into four sections, as follows.

Section I: Low-Complexity Linear Precoding for PAPR reduction in Massive MU-MIMO-OFDM Downlink Systems

we developed a downlink transmission scheme to address the PAPR reduction problem for OFDM-based massive MIMO wireless systems, which leaves the processing required at each mobile terminal untouched while affecting only the signal processing at the BS. Our proposed method performs jointly MU precoding and PAPR reduction that are formulated as a convex optimization problem and solved online with instantaneous processed data via gradient descent (GD) approach.

This work has been published in: R. Zayani, H. Shaiek and D. Roviras, "PAPR-Aware Massive MIMO-OFDM Downlink," in IEEE Access, vol. 7, pp. 25474-25484, 2019, doi: 10.1109/ACCESS.2019.2900128.

Section II: Joint MU Precoding and Energy-Eciency enhancement Algorithm

We introduced a new downlink transmission approach to address the power amplifier (PA) nonlinearity issue in wireless massive MU-MIMO systems. We introduce a PA-aware precoding scheme that is formulated as a simple convex optimization problem that enables efficient and reliable algorithm implementations. The aim of the proposed approach is to optimize precoded signals that, when amplified and then passed through the channel, guarantee ideal transmission quality.

R. Zayani, H. Shaïek and D. Roviras, "Efficient Precoding for Massive MIMO Downlink Under PA Nonlinearities," in IEEE Communications Letters, vol. 23, no. 9, pp. 1611-1615, Sept. 2019, doi: 10.1109/LCOMM.2019.2924001.

Section III: Analysis and Cancellation of Inter-Numerology Interference in Massive MIMO-OFDM Systems

We investigated the use of spatial multiplexing of users, sharing the same bandwidth, whose associated numerologies are different. We first introduced a precoding/combiner design that aims to manage the mixed numerologies spectrum sharing (SS) transmission. Then, we analyzed the inter-numerology interference (INI) and derive the theoretical expressions of its radiation pattern in massive MIMO-OFDM downlink/uplink systems.

X. Cheng, R. Zayani, H. Shaiek and D. Roviras, "Analysis and Cancellation of Mixed-Numerologies Interference for Massive MIMO-OFDM UL," in IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 470-474, April 2020, doi: 10.1109/LWC.2019.2959526.

X. Cheng, R. Zayani, H. Shaiek and D. Roviras, "Inter-Numerology Interference Analysis and Cancellation for Massive MIMO-OFDM Downlink Systems," in IEEE Access, vol. 7, pp. 177164-177176, 2019, doi: 10.1109/ACCESS.2019.2957194.

Section IV: Recent contributions

IV.1. Low-Complexity Linear Precoding for Low-PAPR Massive MU-MIMO-OFDM Downlink Systems

In order to optimize the tradeoff between performance and complexity, linear precoders based on matrix polynomials (M-POLY) and gradient-iterative approaches are studied for both data and PCSs precoding. Simulation results reveal that these latter provide satisfactory performance while they need much lower computational complexity compared to classical method in literature.

submitted to : International Journal on Communication Systems (IJCS) 2021

IV.2.Low Complexity Downlink Transmission scheme for massive MU-MIMO-OFDM under PA nonlinearities

The goal is to design a transmission scheme taking into consideration MU precoding, PAPR reduction and DPD, with a special focus the computational complexity. Then, a simple convex optimization problem is formulated to solve the massive MIMO precoding optimization problem considering PA nonlinearities and it is solved via GD approach. Our analysis and simulation results reveal that the proposed transmission scheme provides low computational complexity than the schemes studied in literature while closely approaching their performance, offering interesting insights for the design of energy-efficient massive MIMO-OFDM systems.

submitted to : IEEE transaction on Vehicular Technology
Some previous works have studied low-PAPR precoders for massive MU-MIMO-OFDM. All of these methods exploit the excess DoFs and the large null-space offered by the massive MIMO downlink channel to perform low-PAPR precoding. These methods can achieve substantial PAPR reduction but with scarifying high computational complexity. First, we presented an approach to perform jointly MU Precoding and PAPR reduction. Specifically, we design peak-canceling signals (PCSs) to be added to the frequency-domain precoded data signals, with the goal of reducing the PAPRs of their time-domain counterpart signals. Furthermore, we introduced the MU-PP-GDm algorithm that aims to minimize alternately the objective functions with respect, respectively, to the MU precoding and PAPR reduction. The MU-PP-GDm, which can be seen as an alternate approach using gradient-iterative method based linear precoding, has been shown to achieve satisfactory PAPR performance with lower computational complexity than the other previous works, especially when the number of users is sufficiently high. In addition, to optimize the tradeoff between performance and complexity, we have studied linear precoders based on matrix polynomials (M-POLY) approaches for both data and PCSs precoding. The key idea is to approximate the matrix inverse by a matrix polynomial decomposition with ‘J’ terms, where the approximation accuracy can be guaranteed with very few terms.
Low-PAPR Massive MU-MIMO-OFDM