A Comparative Analysis On 5G Cell Free Massive Mimo In Next Generation Networking Environment

Dhalbisoi, Sunita; Rout, Ashima; Sahoo, Ramesh K.; Sethi, Srinivas

doi:10.1109/iciccsp53532.2022.9862350

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The proposed clustering method in this subsection uses DCC, which has been investigated in CF-mMIMO systems, as well as cellular DAS [54], [55]. DCC is a method of user-centric clustering initially proposed for MIMO networks.…”

Section: ) Clustering Methods For Multi-cpu Cf-mmimo Systemsmentioning

confidence: 99%

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

Ito,

Fukue,

Ando

et al. 2024

IEEE Access

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This paper addresses the joint design of a beamformer and user-centric clustering for scalable cell-free massive multiple-input multiple-output (CF-mMIMO) under severe quantization noise generated in backhaul links for central processing units (CPUs) cooperation. The system model comes up with a plan in which multiple CPUs exchange physical layer data under limited bandwidth to enhance performance while introducing clustering across multiple CPUs. We derive the joint optimization of the minimum meansquare error (MMSE) beamformer and user-centric clustering under quantization noise, and propose its lowcomplexity design. The superiority of our proposed design method is clarified by comparing its performance with cellular distributed MIMO systems. Throughout the paper, we first answer the problem of how much gain CF-mMIMO systems with multiple CPUs cooperation can obtain.

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Section: ) Clustering Methods For Multi-cpu Cf-mmimo Systemsmentioning

confidence: 99%

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

Ito,

Fukue,

Ando

et al. 2024

IEEE Access

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“…Recent research on CFm-MIMO is expansive [2], [8], [9], [16]- [20]. While, the authors in [2] investigated the throughput analysis and characterization of the packet error probability for the half-duplex (HD) CFm-MIMO system under the URLLC regime, the study in [8] demonstrated significant throughput improvements and enhanced immunity to spatial fading, outperforming traditional small-cell approaches.…”

Section: A Background Work and Motivationmentioning

confidence: 99%

“…The integration of URLLC with cutting-edge 6G innovations, like cellfree massive MIMO (CFm-MIMO), becomes indispensable. CFm-MIMO combines the advantages of massive MIMO with a decentralized approach, resolving challenges related to inter-cell handovers, shadowing, and signaling interference, which are particularly significant in mobile vehicular contexts [8], [9]. However, the very essence of CFm-MIMO poses an intrinsic predicament: the need to exchange vast amounts of data between multiple APs and the central processing unit (CPU), often resulting in overwhelming bandwidth demands and increased latency on the fronthaul links.…”

Section: Introductionmentioning

confidence: 99%

URLLC-Enabled Full-Duplex Cell-Free Massive MIMO Systems With Mobility

Kurma,

Singh,

Sharma

et al. 2024

IEEE Open J. Commun. Soc.

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In the rapidly evolving domain of intelligent vehicular systems, reliable and instantaneous communication is paramount. This paper delves into the robustness of an ultra-reliable low-latency communication (URLLC)-enabled full-duplex cell-free massive multiple-input multiple-output (FD-CFm-MIMO) system, considering the unique challenges presented by mobile vehicular networks. Recognizing the constraints of real-world fronthaul links, we focus on a uniform fronthaul quantization strategy. Our primary contribution is deriving the cumulative distribution function (CDF) of the signal-to-interferenceplus-noise ratio (SINR) at each receiver. We utilize the Welch-Satterthwaite approximation, taking into account the effects of imperfect channel state information (CSI) and the inherent mobility of vehicle communication units (VCUs). Building upon this, we present closed-form expressions of outage probability for both infinite and finite block-length transmission models. A comparative study using Monte Carlo simulations validates our analytical derivations. We consider the half-duplex CFm-MIMO system as a benchmark scheme. Our results underscore the significant influence of several system parameters, such as transmit power, VCU mobility, CSI accuracy, fronthaul link quality, residual interference (RI), and quantization nuances, Doppler power spectra models, pilot contamination on overall system performance.INDEX TERMS Outage probability, cell-free massive multiple-input and multiple-output (CFm-MIMO), confined fronthaul link, ultra-reliable low latency communication (URLLC), vehicle communication, mobility.

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“…Cell-free technology [1][2][3] enables the deployment of large-scale MIMO systems without cell boundaries, leading to enhanced network coverage, increased capacity, and reduced interference levels. This approach enables more flexible and efficient power allocation, leading to improved overall network performance.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

Lin,

Chang,

Tseng

2023

Symmetry

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We propose a deep learning-based cross-layer power allocation method for asymmetric cell-free massive MIMO video communication systems. The proposed cross-layer approach considers physical layer channel state information (CSI) and the application layer rate distortion (RD) function, and it aims to enhance video quality in terms of peak signal-to-noise ratio (PSNR). Our study develops a decentralized deep neural network (DNN) model to capture intricate system patterns, enabling accurate and efficient power allocation decisions. The proposed cross-layer approach includes unsupervised and hybrid (supervised/unsupervised) learning models. The numerical results show that the hybrid method achieves convergence with just 50% of the iterations required by the unsupervised learning model and that it achieves a 1 dB gain in PSNR over the baseline physical layer scheme.

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A Comparative Analysis On 5G Cell Free Massive Mimo In Next Generation Networking Environment

Cited by 8 publications

References 14 publications

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

URLLC-Enabled Full-Duplex Cell-Free Massive MIMO Systems With Mobility

Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

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