Comparison and analysis of massive MIMO linear precoding schemes in the downlink

Mukubwa, Emmanuel; Sokoya, O.; Ilčev, Dimov Stojče

doi:10.1109/afrcon.2017.8095479

Cited by 5 publications

(10 citation statements)

References 26 publications

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“…In this way, just heuristic precoding strategies are achievable in fading multi‐cell networks. RNA is a state‐of‐the‐art heuristic scheme with a simple closed‐form precoding expression based on MMSE precoding [10].…”

Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

“…It is stated in [10] that when the value of the power of the polynomial,

J_{j}

, is low the performance of the TPE precoder is so poor, but as the value of

J_{j}

increases, its bit‐rates improve and nears that of RNA precoder. This comes at the cost of increase in the hardware needed for the implementation of the TPE precoding.…”

Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

“…RNA precoding will have relatively the same complexity as TPE precoding at the specified hardware implementation while giving a better performance. This means that the increase in hardware to improve the performance of TPE precoder will suffice the RNA precoder to reduce its complexity and come as close to the complexity of the TPE precoder but with a better performance in terms of throughput [9, 10, 24]. Using the notation of [18], the RNA precoding matrix used by the BS in the j th cell is

ω_{j}^{RNA} = \sqrt{ϑ_{j}} {(\sum_{ℓ = 1}^{L} \sum_{k = 1}^{K} {\hat{h}}_{ℓ, j, k} {\overset{false^}{bold-italich}}_{ℓ, j, k}^{H} + Z_{j})}^{- 1} {\hat{h}}_{j, j, k}

where

Z = \sum_{k = 1}^{K} ()(, {boldΦ}_{k} - {bold-italicR}_{k}) + false(1 / ρ_{t r} false) {bold-italicI}_{M}

and the scalar

ϑ_{j} > 0

is carefully chosen to satisfy the power constraint in the cell.…”

Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

“…Thus, the total complexity was computed as follows:

\sum_{n = 0}^{J_{j} - 1} ()(, \frac{()(, 3 K_{j}^{2} + K_{j}^{}) M_{j}^{}}{2} + K_{j}^{3}) + 7 K_{j}^{}

From (19), the combining complexity of the RNA precoder can be evaluated based on the Appendix. It was pointed out in [10] that the consideration of the initial three terms gives the quickest convergence of the iterative process for finding the inverse in RNA. The term

{bold-italicZ}_{j}

is presumed to be available for free at j th BS.…”

Section: Energy Efficiency (Ee)mentioning

confidence: 99%

“…We cannot overlook the complexity of computing the minimum mean square error (MMSE) precoding in massive MIMO network system, since MMSE relies on inverting of substantial matrices [9]. To circumvent this, a class of rapid numerical algorithms (RNA) and truncated polynomial expansion (TPE) precoders were discussed in [10] for the single cell multi‐user case. The multicell TPE‐based precoder is discussed in [11] and compared to regularised zero forcing (RZF) precoding.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Mukubwa¹,

Sokoya²

2020

J. eng.

Self Cite

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Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

“…It is stated in [10] that when the value of the power of the polynomial,

J_{j}

, is low the performance of the TPE precoder is so poor, but as the value of

J_{j}

increases, its bit‐rates improve and nears that of RNA precoder. This comes at the cost of increase in the hardware needed for the implementation of the TPE precoding.…”

Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

ω_{j}^{RNA} = \sqrt{ϑ_{j}} {(\sum_{ℓ = 1}^{L} \sum_{k = 1}^{K} {\hat{h}}_{ℓ, j, k} {\overset{false^}{bold-italich}}_{ℓ, j, k}^{H} + Z_{j})}^{- 1} {\hat{h}}_{j, j, k}

where

Z = \sum_{k = 1}^{K} ()(, {boldΦ}_{k} - {bold-italicR}_{k}) + false(1 / ρ_{t r} false) {bold-italicI}_{M}

and the scalar

ϑ_{j} > 0

is carefully chosen to satisfy the power constraint in the cell.…”

Section: Multi‐cell Linear Precodingmentioning

confidence: 99%

“…Thus, the total complexity was computed as follows:

\sum_{n = 0}^{J_{j} - 1} ()(, \frac{()(, 3 K_{j}^{2} + K_{j}^{}) M_{j}^{}}{2} + K_{j}^{3}) + 7 K_{j}^{}

{bold-italicZ}_{j}

is presumed to be available for free at j th BS.…”

Section: Energy Efficiency (Ee)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Mukubwa¹,

Sokoya²

2020

J. eng.

Self Cite

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Analysis of Precoding-Based Pilot Decontamination Methods in Massive MIMO Systems

Misso

Maiseli

Kissaka

2020

Proceedings of Fifth International Congress on Information and Communication Technology

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Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

Soufy

Nashwan

Al-Kamali

et al. 2023

The Journal of Engineering

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Massive multiple-input multiple-output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero-forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal-to-noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

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Comparison and analysis of massive MIMO linear precoding schemes in the downlink

Cited by 5 publications

References 26 publications

Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Analysis of Precoding-Based Pilot Decontamination Methods in Massive MIMO Systems

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

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