Signal processing for massive MIMO communications

Khandaker, Muhammad R. A.; Wong, Kai-Kit

doi:10.1016/b978-0-12-811887-0.00008-0

Cited by 3 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. z K ] = H, which matches the MRT precoding [24]. Back to the proposed algorithm, for each H t (13), one evaluates expression (14) as…”

Section: B Precoding Methodsmentioning

confidence: 99%

“…The proposed algorithm leverages existing I-CSI -based precoding schemes (such as those in [23], [24]), but here, they are fed with random channel vectors. The random channel vectors are generated using statistics obtained from the channel history of the URLLC link, specifically, the sample mean m and sample covariance matrix C, which are given by…”

Section: A Proposed Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Pérez

López

Alves

2023

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement's history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interferenceplus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10 −3 are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

show abstract

“…. z K ] = H, which matches the MRT precoding [24]. Back to the proposed algorithm, for each H t (13), one evaluates expression (14) as…”

Section: B Precoding Methodsmentioning

confidence: 99%

Section: A Proposed Algorithmmentioning

confidence: 99%

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Pérez

López

Alves

2023

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…It is also known as large-scale antenna systems and promises significant gains in terms of spectral efficiency (SE) and energy efficiency (EE) to accommodate a large number of users at extraordinarily high data rates with better reliability while consuming much less power. Ma-MIMO is one of the exciting technologies enabling the fifth generation (5 G) and beyond fifth generation (B5G) wireless cellular networks (Khandaker & Wong, 2018;Ngo et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of joint transmit antenna selection and user scheduling for massive MIMO systems

Mengistu

Worku

2021

Cogent Engineering

View full text Add to dashboard Cite

To alleviate the problem of hardware complexity, energy wastage and increased cost of deployment in massive multiple input multiple output (MIMO) system, joint antenna selection and user scheduling (JASUS) algorithms are proposed which reduce the computational complexity of optimal exhaustive search algorithm with a scarification of some spectral efficiency (SE). These algorithms remove the worst performance antenna greedily and results in best set of antennas and users at the same time. But all JASUS algorithms use semi-orthogonal user scheduling (SUS) to select users and zero forcing (ZF) precoding to mitigate co-channel interferences. A semi-orthogonal user scheduling scheme generates a high computational complexity for massive MIMO systems. Therefore, in this work with the objective of reducing the complexity of SUS, we implement two low complexity user scheduling in JASUS algorithm. These are norm-based user scheduling (NUS) and random user scheduling (RUS). Also, we apply minimum mean square error (MMSE) and maximum ratio transmission (MRT) precoding techniques along with user scheduling schemes to examine their SE and computational complexity performance when implemented in JASUS algorithm. Compared with the originally implemented SUS user scheduling technique, NUS showed a slightly better SE performance under all precoding schemes with a much reduced complexity. RUS shows around 3 bits/s/Hz performance decrement with much less computational complexity as compared to SUS in JASUS algorithm. Also, performance

show abstract

Performance analysis of BS-NOMA for mmWave communication using lens antenna array

Mengistu

Mihrete

2022

Cogent Engineering

View full text Add to dashboard Cite

Signal processing for massive MIMO communications

Cited by 3 publications

References 66 publications

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Performance analysis of joint transmit antenna selection and user scheduling for massive MIMO systems

Performance analysis of BS-NOMA for mmWave communication using lens antenna array

Contact Info

Product

Resources

About