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

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

doi:10.1109/twc.2017.2777987

Cited by 78 publications

(51 citation statements)

References 34 publications

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“…On the other hand, it is proposed that multicast delivery can be useful for reducing the energy consumption and thereby prolonging the lifetime of wireless multi-hop networks [24]. Transmission and resource allocation are studied for several popular communication systems including multiple-input-multiple-output (MIMO) [25], [26], orthogonal frequency-division multiplexing (OFDM) communication [27], [28], and nonorthogonal multiple access (NOMA) [29], [30]. For MIMO multicasting, researchers have studied the joint design of clustering and multicast beamforming [25], as well as the multicast precoding design accounting for user fairness [26].…”

Section: B Multicasting In Wireless Networkmentioning

confidence: 99%

Spectrum Allocation in Wireless Networks for Crowd Labelling

Zhu

Shen

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The realistic adoption of this technique however entails labelling of the enormous data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new perspective of wireless crowd labelling that is capable of downloading data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. In this cross-disciplinary area, the integration of the rate-distortion theory and the principle of repetition labelling for accuracy improvement gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy.Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios. Thereby, the optimal solution can be found by an efficient tree search. Next, the solution is simplified by applying truncated channel inversion. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming.In addition, exact polices are derived for the annotators constrained and spectrum constrained cases. Last, simulation results demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

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Section: B Multicasting In Wireless Networkmentioning

confidence: 99%

Spectrum Allocation in Wireless Networks for Crowd Labelling

Zhu

Shen

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…, G}, is of interest for K j UTs, and we say that these K j UTs belong to the jth multicasting group. We denote the set of indices of all the UTs in the jth multicasting 1 The difference between multicast transmission and unicast transmission is briefly explained in Section II-B.…”

Section: System and Signal Modelmentioning

confidence: 99%

“…The first author was visiting Linköping University when the work was performed. A more comprehensive version of this work has been submitted to IEEE Transactions on Wireless Communications [1]. vectors that maximize the minimum value of a quality of service metric, given a fixed power budget [6].…”

Section: Introductionmentioning

confidence: 99%

Multigroup Multicast Precoding in Massive MIMO

Sadeghi

Bjoernson

Larsson

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Optimal physical layer multicasting (PLM) is an NP-hard problem that for simplicity has been studied under idealistic assumptions, e.g., availability of perfect channel state information (CSI), both at the base station (BS) and at the user terminals (UTs). With the advent of massive multiple-inputmultiple-output (MIMO), PLM has become more challenging, as the computational complexity of the precoder design is proportional to the number of BS antennas. In this paper, we address these issues by introducing computationally efficient precoders that account for practical CSI acquisition. We derive achievable spectral efficiency expressions for the proposed precoders. Then we introduce a novel problem formulation for the max-min fairness power control that accounts the CSI acquisition overhead, uplink training and downlink transmission powers. We solve this problem and find the optimal uplink and downlink power control policies in closed form. Using numerical simulations, we verify the effectiveness of our proposed schemes compared to the-stateof-the-art PLM schemes for massive MIMO systems.Index Terms-Physical layer multicasting, Massive MIMO, beamforming, large-scale antenna systems.

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“…The QoS problem is also considered in [8]- [10] with diverse extensions to the formulation. A related formulation known as the maxmin fair (MMF) problem is studied in [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Precoding for Multi-Group Multicasting in mmWave Systems

Abanto-Leon

Hollick

Sim

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Multicast beamforming is known to improve spectral efficiency. However, its benefits and challenges for hybrid precoders design in millimeter-wave (mmWave) systems remain understudied. To this end, this paper investigates the first joint design of hybrid transmit precoders (with an arbitrary number of finite-resolution phase shifts) and receive combiners for mmWave multi-group multicasting. Our proposed design leverages semidefinite relaxation (SDR), alternating optimization and Cholesky matrix factorization to sequentially optimize the digital/analog precoders at the transmitter and the combiners at each receiver. By considering receivers with multiple-antenna architecture, our design remarkably improves the overall system performance. Specifically, with only two receive antennas the average transmit power per received message improves by 16.8% while the successful information reception is boosted by 60%. We demonstrate by means of extensive simulations that our hybrid precoder design performs very close to its fully-digital counterpart even under challenging scenarios (i.e., when colocated users belong to distinct multicast groups).

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

Cited by 78 publications

References 34 publications

Spectrum Allocation in Wireless Networks for Crowd Labelling

Spectrum Allocation in Wireless Networks for Crowd Labelling

Multigroup Multicast Precoding in Massive MIMO

Hybrid Precoding for Multi-Group Multicasting in mmWave Systems

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