Multi-Cell Massive MIMO Uplink With Underlay Spectrum Sharing

Al-Hraishawi, Hayder; Baduge, Gayan Amarasuriya Aruma; Ngo, Hien Quoc; Larsson, Erik G.

doi:10.1109/tccn.2018.2877998

Cited by 21 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BSs in [21] use the maximum ratio transmission (MRT) for signal transmission, and the DL achievable rate of the secondary system which is underlaid in a primary massive MIMO system is investigated. Reference [20] investigates the impact of inherent intra/inter-cell pilot contamination in multi-cell multi-user massive MIMO system with underlay spectrum sharing. In [22], a dual-hop enabled spectrum sharing system is analyzed, and the achievable rates are derived.…”

Section: A Related Prior Research On Underlay Spectrum Sharing With M...mentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Underlay Spectrum Sharing in Cell-Free Massive MIMO Systems

Galappaththige¹,

Baduge²

2021

Preprint

View full text Add to dashboard Cite

We investigate the coexistence of underlay spectrum sharing in cell-free massive multiple-input multipleoutput (MIMO) systems. A primary system with geographically distributed primary access points (P-APs) serves a multitude of primary users (PUs), while a secondary system serves a large number of secondary users (SUs) in the same primary/licensed spectrum by exploiting the underlay spectrum sharing. To mitigate the secondary cochannel interference inflected at PUs, stringent secondary transmit power constraints are defined for the secondary access points (S-APs). A generalized pilots sharing scheme is used to locally estimate the uplink channels at P-APs/S-APs, and thereby, conjugate precoders are adopted to serve PUs/SUs in the same time-frequency resource element. Moreover, the effect of a user-centric AP clustering scheme is investigated by assigning a suitable set of APs to a particular user. The impact of estimated downlink (DL) channels at PUs/SUs via DL pilots beamformed by P-APs/S-APs is investigated. The achievable primary/secondary rates at PUs/SUs are derived for the statistical DL and estimated DL CSI cases. User-fairness for PUs/SUs is achieved by designing efficient transmit power control policies based on a multi-objective optimization problem formulation of joint underlay spectrum sharing and max-min criteria. The proposed orthogonal multiple-access based analytical framework is also extended to facilitate non-orthogonal multiple-access. Our analysis and numerical results manifest that the primary/secondary performance of underlay spectrum sharing can be boosted by virtue of the average reduction of transmit powers/path-losses, uniform coverage/service, and macro-diversity gains, which are inherent to distributed transmissions/receptions of cell-free massive MIMO. I. INTRODUCTION Massive multiple-input multiple-output (MIMO) operating in sub-6 GHz can simultaneously serve many users in the same time-frequency resource element by virtue of aggressive spatial multiplexing gains rendered by large base-station (BS) antenna arrays [1]-[4]. Co-located massive MIMO in which all BS antennas are packed into the same array is currently being deployed in the United States [5]. Thus, the co-located massive MIMO enabled with fully-digital beamforming has already become a reality [6].

show abstract

Section: A Related Prior Research On Underlay Spectrum Sharing With M...mentioning

confidence: 99%

“…The aforementioned related prior references [20]- [24], [26], [29] have investigated the coexistence of massive MIMO and underlay spectrum sharing with only co-located antenna arrays at the BSs.…”

Section: B Our Motivationmentioning

confidence: 99%

Exploiting Underlay Spectrum Sharing in Cell-Free Massive MIMO Systems

Galappaththige¹,

Baduge²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As explained previously, accurate channel estimation is one of the essential prerequisites for the correct operation of RIS. In this context, pilot contamination is one of the most performance detrimental issues in large antenna-array systems, e.g., massive MIMO systems [51], [52]. Due to a large number of reflective elements, which resemble passive antennas, this problem occurs in RIS-assisted networks as well.…”

Section: A Pilot Decontaminationmentioning

confidence: 99%

Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

Kisseleff

Martins

Al-Hraishawi

et al. 2020

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

“…Spectrum sharing in multicell MIMO is, nonetheless, not well understood. In [13], this was investigated in underlay spectrum sharing systems and the aim was to quantify the achievable rate performance due to ICI and other imperfections.…”

Section: Introductionmentioning

confidence: 99%

Truly Distributed Multicell Multi-Band Multiuser MIMO by Synergizing Game Theory and Deep Learning

et al. 2021

View full text Add to dashboard Cite

Dynamic frequency allocation (DFA) with massive multiple-input multiple-output (MIMO) is a promising candidate for multicell communications where massive MIMO is adopted to maximize the per-cell capacity whereas the inter-cell interference (ICI) is tackled by DFA. Realizing this approach in a distributed fashion is however very difficult due to the lack of global channel state available at the base stations (BSs) in the cell level. We utilize a forward-looking game to automate reconciliation for DFA in a distributed manner between cells while zero-forcing (ZF) is used at each cell to maximize the multiplexing gain. To maximize the network capacity, multi-agent deep reinforcement learning (DRL) using offline centralized training is leveraged to train the BSs to master their game-theoretic reconciliation strategies. The result is a trained neural network for each BS, empowering it with rich experience of reconciliation with other BSs for converging to a network-efficient equilibrium. The online algorithm is distributed with the BSs competing as expert players to start the negotiation process using their trained actions. Simulation results show that the proposed synergized deep-learning game-theoretic algorithm outperforms significantly the DRL-only and game-theoretic only methods, and other benchmarks for multicell MIMO.

show abstract

Multi-Cell Massive MIMO Uplink With Underlay Spectrum Sharing

Cited by 21 publications

References 31 publications

Exploiting Underlay Spectrum Sharing in Cell-Free Massive MIMO Systems

Exploiting Underlay Spectrum Sharing in Cell-Free Massive MIMO Systems

Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities

Truly Distributed Multicell Multi-Band Multiuser MIMO by Synergizing Game Theory and Deep Learning

Contact Info

Product

Resources

About