Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas

By Hoon Huh, Giuseppe Caire, Haralabos C. Papadopoulos, Sean A. Ramprashad. IEEE Transactions on Wireless Communications, 2011

416 citations recorded by the source corpus.

Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal uplink pilot signals, by exploiting channel reciprocity. Based on this observation, a recently proposed "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of distance-dependent pathloss and channel coherence time and bandwidth. The main focus and contribution of this paper is an improved Network-MIMO TDD architecture achieving spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell (roughly, from 500 to 50 antennas). The proposed architecture is based on a family of Network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference mitigation constraints, uplink pilot signals allocation and frequency reuse across cells. The key idea consists of partitioning the users into equivalence classes, optimizing the Network-MIMO scheme for each equivalence class, and letting a scheduler allocate the channel time-frequency dimensions to the different classes in order to maximize a suitable network utility function that captures a desired notion of fairness. This results in a mixed-mode Network-MIMO architecture, where different schemes, each of which is optimized for the served user equivalence class, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a systematic and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/429adc2d1c95fdd39915ee00eaafc0d65a056f6f

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Citations in the corpus

Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination (2019, background): “Nevertheless, this research has now become mature, thanks to fundamental works such as [21]–[24], [26] and the recent developments in [19], [20], [25] that have provided the last important pieces to the puzzle.”
An Overview of Massive MIMO: Benefits and Challenges (2014, neutral): “The network-MIMO-based scheme proposed in [40]”
Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations (2014, background): “This technology promises signiﬁcant improvements in terms of spectral efﬁciency, link reliability, and coverage compared to conventional (small-scale) systems [9], [11], [12].”
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer? (2014, background): “The statistical information carried in these matrices can be utilized in the scheduler to ﬁnd statistically compatible users that are likely to interfere less with each other [37].”
Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need? (2013, background): “3Note that similar assumptions have been made in [16], [17].”
Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling (2014, methodology): “This work was supported by the IT R&D program ofMSIP/ KEIT.”
Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation (2014, background): “A viable solution to reduce the training overhead is the adoption of a cellular network architecture based on TDD, as discussed in [283], [284], and [286]–[288].”
Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO With Stochastic Geometry (2017, methodology): “The capacity of Massive MIMO systems with spatially correlated channels and imperfect CSI has been extensively analyzed in [17], [19]–[21].”
Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated? (2014, background): “A main insight is that the limited number of orthogonal pilot sequences needs to be allocated intelligently among the UEs to reduce interference, which can be done by capitalizing on pathloss differences [16], [17] and spatial correlation [15], [18], [19].”
Massive MIMO: An Introduction (2015, neutral): “Signal processing algorithms and modulation formats are attracting much attention [31–36].”
Fundamental Limits of Cooperation (2012, background): “The points made in the sequel, which are consistent with other recent works [34]–[36], descend from the model detailed in Section II and its subsequent analysis in Sections III and IV.”