Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems

By Hien Quoc Ngo, Erik G. Larsson, T. Marzetta. IEEE Transactions on Communications, 2011

3,036 citations recorded by the source corpus.

A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. An MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified for a channel model that includes small-scale fading but not large-scale fading. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8869b50a14b16d281d0763ea483cb6025d2186de

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Citations in the corpus

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User Association in 5G Networks: A Survey and an Outlook (2015, methodology): “Table V qualitatively compares the existing user association algorithms proposed for mmWave networks.”
Multipair Full-Duplex Relaying With Massive Arrays and Linear Processing (2014, neutral): “The MRC receiver and MRT precoding matrices are respectively given by [22], [23]”
Aspects of favorable propagation in Massive MIMO (2014, methodology): “e beneﬁts of traditional MIMO, but at a much larger scale. More precisely, massive MIMO systems can provide high throughput, communication reliability, and high power efﬁciency with linear processing [4]. One of the key assumptions exploited by massive MIMO is that the channel vectors between the ba”
A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, background): “As far as energy efﬁciency is concerned, massive MIMO has been shown to reduce the radiated power by a factor proportional to the square root of the number of deployed antennas, while keeping the information rate unaltered [81].”
An Overview of Signal Processing Techniques for Terahertz Communications (2020, background): “ion and a few antennas at the receiving equipment, channel hardening occurs. With channel hardening, simple linear detection schemes such as zero-forcing and MMSE can achieve near-optimal performance [345]. This is not the case in THz systems, where symmetric doubly-massive MIMO systems are common [”
Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming (2018, methodology): “Proof: Since ‖g‖ 2 N → (2)g and ‖hr‖ 2 N → (2)h as N → ∞ [10], it follows that”
Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated? (2014, background): “1A few results for single-cell systems are available in the literature; for example, in [8].”
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].”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, methodology): “ive reﬂecting elements to that of M-MIMO or MIMO relay with the same number (N) of active antennas. For M-MIMO, it is well known that a transmit/receive beamforming gain of order O(N) can be achieved [55], [56]. While it was shown in [8] that even with perfect selfinterference cancellation, the rece”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “At the physical layer, the technology innovations to support these data rates include communications in the mmWave frequency band [47], wherein the large bandwidth can support high data rates; M-MIMO whereby the number of antenna elements at the Base Station (BS) is much larger in proportion to the ”
Massive MIMO for next generation wireless systems (2013, methodology): “3 (from [6]) depicts the fundamental tradeoff between the energy efficiency in terms of the total number of bits (sum-rate) transmitted per Joule per terminal receiving service of energy spent, and spectral efficiency in terms of total number of bits (sum-rate) transmitted per unit of radio spectrum”
A Survey of Device-to-Device Communications: Research Issues and Challenges (2018, neutral): “interference [253], [254].”
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks (2016, background): “Massive channel information acquisition and the associated pilot power consumption are the ﬁrst challenge since the resources for channel state acquisition is in general limited and the accuracy of the channel estimation will directly affect the achievable throughput as shown in [120].”
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