Multigroup Multicast Precoding in Massive MIMO

Sadeghi, Meysam; Bjoernson, Emil; Larsson, Erik G.; Yuen, Chau; Marzetta, Thomas L.

doi:10.1109/glocom.2017.8254889

Cited by 6 publications

(15 citation statements)

References 19 publications

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“…where in (e) we used the independency off m and w ZF g . Therefore, the achievable SE and the effective SINR of unicast UT m with ZF precoding are 13 Note that (34) and (35) clearly show the relation between the achievable SE and the system parameters. For example, (35) shows that the coherent beamforming gain is N − G − U , the effect of power allocation and the effective channel gain experienced by the UT is p dl m ϑ m , and the interference experienced by the UT due to joint unicast and multicast transmission is (β m − ϑ m )(P un + P mu ).…”

Section: B Achievable Spectral Efficiency For Zf Precodermentioning

confidence: 96%

“…Consider the unicast UTs. Starting from (7) and applying (12), (13), and f u =f u −f u , we can write the signal received by mth unicast UT as…”

Section: B Achievable Spectral Efficiency For Zf Precodermentioning

confidence: 99%

“…The first approach exploits the asymptotic orthogonality of the channels in massive MIMO systems to simplify the MMF problem [10], [11]. However, it requires an extremely large number of antennas to provide a reasonable performance, e.g., N > 1000 [11], [13]. The insufficiency of the asymptotic approach is detailed in [11], [13].…”

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confidence: 99%

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Joint Unicast and Multi-Group Multicast Transmission in Massive MIMO Systems

Sadeghi

Björnson

Larsson

et al. 2018

IEEE Trans. Wireless Commun.

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We study the joint unicast and multi-group multicast transmission in massive multiple-input-multipleoutput (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

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Section: B Achievable Spectral Efficiency For Zf Precodermentioning

confidence: 96%

“…Consider the unicast UTs. Starting from (7) and applying (12), (13), and f u =f u −f u , we can write the signal received by mth unicast UT as…”

Section: B Achievable Spectral Efficiency For Zf Precodermentioning

confidence: 99%

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Joint Unicast and Multi-Group Multicast Transmission in Massive MIMO Systems

Sadeghi

Björnson

Larsson

et al. 2018

IEEE Trans. Wireless Commun.

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“…A relayassisted MIMO precoder design for a multicast system have been proposed in [5]. Precoder for multi-group multicasting systems have been discussed in [6], [7]. However, all these works consider a single transmitting BS, while in MCC, clusters of BSs are coordinating to serve common data to all group users.…”

Section: Introductionmentioning

confidence: 99%

Multi-Cell MIMO Transceiver Design for Mission-Critical Communication

Jagyasi

Coupechoux

Daher³

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Business and Mission critical communication (MCC) is a major communication paradigm that is used by public agencies, e.g., during emergency situations, or critical infrastructure companies, e.g., airports, transportation, etc. MCC has very stringent requirements in terms of reliability, coverage and should offer group communications. Coordinated Multimedia Multicast/Broadcast single frequency network (MBSFN) is considered as a potential technology for MCC as it benefits from increased coverage and inter-cell interference mitigation. In this paper, we propose multi-input-multi-output (MIMO) multimedia MBSFN system design wherein each base station (BS) of a coordinated cluster multicasts a common message to all the users in a group. We use a greedy algorithm to dynamically form the cluster of synchronized BSs for optimal utilization of resources within an MBSFN. We assume the availability of perfect channel state information (CSI) knowledge and jointly obtain the optimal precoder and receive filters by minimizing the overall sum-meansquare-error (sum-MSE) constrained over the total transmit power. We further extend the proposed design to a robust case by considering the imperfections in available channel knowledge and obtain the transceiver matrices that are resilient to channel errors. We also present both the joint and robust system design for Single-Cell point-to-multipoint (SC-PTM) which is an alternative solution to MBSFN in MCC. Numerical results show the effectiveness of the proposed network architecture for future mission critical communication. Furthermore, the comparison results show that the proposed robust design demonstrate better performance and is resilient to the presence of CSI errors.

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“…Considering a multicasting system with an N -antenna BS and G different multicasting groups, the complexity of SDR based techniques is of O(G 3.5 N 6.5 ) [7]. This high complexity makes the SDR based multicasting 1 We present a formal definition of unicast and multicast transmissions in Section II. B.…”

Section: Introductionmentioning

confidence: 99%

Max–Min Fair Transmit Precoding for Multi-Group Multicasting in Massive MIMO

Sadeghi

Björnson

Larsson

et al. 2018

IEEE Trans. Wireless Commun.

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This paper considers the downlink precoding for physical layer multicasting in massive multipleinput-multiple-output (MIMO) systems. We study the max-min fairness (MMF) problem, where channel state information (CSI) at the transmitter is used to design precoding vectors that maximize the minimum spectral efficiency (SE) of the system, given fixed power budgets for uplink training and downlink transmission. Our system model accounts for channel estimation, pilot contamination, arbitrary pathlosses, and multi-group multicasting. We consider six scenarios with different transmission technologies (unicast and multicast), different pilot assignment strategies (dedicated or shared pilot assignments), and different precoding schemes (maximum ratio transmission and zero forcing), and derive achievable spectral efficiencies for all possible combinations. Then we solve the MMF problem for each of these scenarios and for any given pilot length we find the SE maximizing uplink pilot and downlink data transmission policies, all in closed-forms. We use these results to draw a general guideline for massive MIMO multicasting design, where for a given number of base station antennas, number of users, and coherence interval length, we determine the multicasting scheme that shall be used. Index TermsMulticast transmission, Massive MIMO, physical layer precoding, large-scale antenna systems.

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Multigroup Multicast Precoding in Massive MIMO

Cited by 6 publications

References 19 publications

Joint Unicast and Multi-Group Multicast Transmission in Massive MIMO Systems

Joint Unicast and Multi-Group Multicast Transmission in Massive MIMO Systems

Multi-Cell MIMO Transceiver Design for Mission-Critical Communication

Max–Min Fair Transmit Precoding for Multi-Group Multicasting in Massive MIMO

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