Hybrid Beamforming for Millimeter Wave Massive MIMO under Multicell Multiuser Environment

Abose, Tadele A; Olwal, Thomas O.; Hassen, Murad R

doi:10.17485/ijst/v15i20.114

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The concept of cell-free network in wireless communication, which is the core technology of sixth-generation (6G), is coined from small-cells (SC), distributed MIMO, and massive MIMO (4,5) . The CFMM systems reaps the benefits of network MIMO, small cells (SC) and massive MIMO (6)(7)(8) . In CFMM system processing unit is separate i.e.…”

Section: Introductionmentioning

confidence: 99%

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Deshpande,

Aggarwal,

Sabherwal

et al. 2024

IJST

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Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

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Section: Introductionmentioning

confidence: 99%

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Deshpande,

Aggarwal,

Sabherwal

et al. 2024

IJST

View full text Add to dashboard Cite

show abstract

“…The Cell-Free Massive Multiple Input Multiple Output (CFMM) system is the core technology for the upcoming sixth-generation (6G) networks, for deploying massive multiple-input multiple-output (MIMO) systems without the restriction of cells, which provides high data throughput, ultra-low latency, ultra-high reliability, high spectral and energy efficiency and uniform coverage (4,5) . The CFMM systems reaps the benefits of network MIMO, small cells (SC) and massive MIMO (6)(7)(8) . We know that PC and accuracy of CE are inversely proportional to each other i.e., with increase in PC, the accuracy of CE decreases.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks

Deshpande,

Aggarwal,

Sabherwal

et al. 2024

IJST

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Objectives: The performance of Cell-Free Massive Multiple Input Multiple Output (CFMM) is analyzed in this paper for its two bottlenecks i.e., Pilot Contamination (PC) and Channel Estimation Error (CEE). Methods: The CFMM network is strongly affected by PC which is one of the bottlenecks due to which quality of service and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting advanced pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing CEE for the CFMM systems to maximize spectral efficiency (SE). We derive achievable uplink and downlink SE expressions for the proposed system, and compare with Minimum Mean Square Error and Maximum Ratio combining techniques. As well, the performance is evaluated for different antenna configurations. The advanced pilot assignment algorithm is compared with greedy pilot assignment and random pilot assignment methods. The performance of cellular massive multiple input multiple output (MIMO) is derived for comparison. The performance of CFMM system is evaluated using MATLAB software. Findings: The UL and DL performance of the proposed system in terms of SE is 3.2 times higher than the conventional CFMM with MMSE and MR combining techniques. Average sum spectral efficiency of the proposed system increases with increase in number of access points (APs). Comparison with different antenna configurations reveals that, with 400 APs equipped with single antenna, only UE with good channel condition shows performance enhancement, but when each AP is equipped with 4 antennas, the UE with unfavourable channel condition also give better performance. Advanced pilot assignment scheme proves to be better than greedy and random pilot assignment techniques. For the same cellular set up, the proposed CFMM system achieves higher SE than the cellular massive MIMO. Novelty: Due to the advanced pilot assignment algorithm used in the proposed CFMM system, at a time, only one AP is selected and the selected AP with its full received power serves the desired UE, which suppresses interference resulting in improved SE performance. The serving AP is selected considering the distance between UE and AP, rather than using large scale fading coefficient which is the unique feature of pilot assignment algorithm. The proposed deep learning-aided channel estimation method, minimizes the mean square error (MSE) between the actual channel and the channel estimates obtained from the MMSE estimation resulting in reduction in channel estimation error. Thus, the use of the proposed advanced pilot assignment algorithm and deep learning-aided channel estimation method increase the SE performance of the CFMM system. Keywords: CellFree Massive Multiple Input Multiple Output, Pilot Contamination, Channel Estimation Error, Minimum Mean Square Error, Maximum Ratio

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CCOA‐AdaLS: Hybrid Beamforming Using Chaotic Chebyshev Aquila Optimization for mmWave Massive MIMO

R.,

Rahman,

et al. 2024

Int J Communication

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This research aims to design hybrid analog and digital beamforming to improve the signal‐to‐noise ratio (SNR) and spectral efficiency (SE) of communication links. Considering the complexity and cost associated with fully connected multiple‐input multiple‐output (MIMO) communication models, a partially connected system model is adopted for the downlink millimeter‐wave (mmWave) communication model. The analog beamforming utilizes the adaptive search (AdaLS) algorithm to minimize interference and enhance user power, whereas digital beamforming is optimized using the proposed Chaotic Chebyshev Aquila Optimization (CCAO) algorithm. The CCAO algorithm integrates chaotic Chebyshev–based solution mapping with the conventional Aquila Optimization Algorithm to enhance exploration capability. The system model is illustrated, and the problem is formulated to maximize the signal‐to‐interference‐plus‐noise ratio (SINR) through the selection of the required signal at the receiver. The digital beamformer is designed using Lagrange's multiplier, and the analog beamformer is optimized using AdaLS. The proposed CCAO algorithm is detailed, incorporating chaotic dynamics to explore the solution space effectively. The research evaluates the performance of the proposed method against conventional approaches, showcasing improved normalized beam gain and SINR.

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Hybrid Beamforming for Millimeter Wave Massive MIMO under Multicell Multiuser Environment

Cited by 5 publications

References 16 publications

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Deep Learning-aided Channel Estimation Combined with Advanced Pilot Assignment Algorithm to Mitigate Pilot Contamination for Cell-Free Networks

CCOA‐AdaLS: Hybrid Beamforming Using Chaotic Chebyshev Aquila Optimization for mmWave Massive MIMO

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