On the complexity of very large multi-user MIMO detection

Čirkić, Mirsad; Larsson, Erik G.

doi:10.1109/spawc.2014.6941316

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…To select appropriate processing algorithms for Massive MIMO is non-trivial, and an analysis of the trade-off between computational complexity and processing performance is necessary. Reference [48] presents such an analysis for different MMSE detection techniques.…”

Section: E Complexity Versus Accuracy Trade-offmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency.Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices.

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Section: E Complexity Versus Accuracy Trade-offmentioning

confidence: 99%

Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions

Perre¹,

Liu²,

Larsson³

2018

IEEE Trans. Signal Process.

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“…In [63], three different techniques for linear MMSE detection are investigated and compared. The first technique is an exact MMSE via LDL decomposition, which decomposes the matrix H H aI into a lower triangular matrix L and a diagonal matrix D that yield LDL H H aI (note that the reference [63] assumes that channel matrix H is real, so that H H ). The advantage of this method is that the inverse of triangular and diagonal matrices is trivial and obtained only with sums and products.…”

Section: A Massive Mimo Baseband Processing Functions 1) Massive Mimo...mentioning

confidence: 99%

A Tutorial on the Characterisation and Modelling of Low Layer Functional Splits for Flexible Radio Access Networks in 5G and Beyond

Pérez-Romero,

Sallent,

Gelonch

et al. 2023

IEEE Commun. Surv. Tutorials

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The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.

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