Machine Learning Aided Hybrid Beamforming in Massive-MIMO Millimeter Wave Systems

Aljumaily, Mustafa S.; Li, Husheng

doi:10.1109/dyspan.2019.8935814

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…In [17], it is shown that hybrid analog-digital beamforming can approach the performance of FD beamforming while requiring lower complexity. In [18], a HBF approach is presented that utilizes machine learning techniques to improve the achievable spectral efficiency (SE). To this end, the proposed two-step algorithm can attain almost the same efficiency as the one that can be achieved by FD architectures.…”

Section: A Related Workmentioning

confidence: 99%

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

et al. 2021

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Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed systemlevel simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

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Section: A Related Workmentioning

confidence: 99%

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

et al. 2021

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“…All these architectures assumed specific work scenarios and environment conditions for their systems to work. On the other hand, machine learning and deep learning techniques have been used to avoid such limitations in building beamforming systems for several years now [3,4,5,8]. The work in [3] is focusing on collaboration of four base stations' (BS) collected data (through directional and omnidirectional pilot signals) to improve the spectral efficiency of a single user that is supposed to be within the coverage area of these four base stations.…”

Section: Related Workmentioning

confidence: 99%

“…In their suggested system, the conventional complex BM-IC algorithm is transformed into a deep neural network (DNN)-based approximation. Authors in [8] developed a new hybrid beamforming system that utilizes a machine learning technique called exact-Radial Basis Function Network (exact-RBFN) to improve the achievable spectral efficiency. First, they used convex optimization to optimize the precoder and combiner components (baseband and phase shifter) weights of the hybrid beamforming system.…”

Section: Related Workmentioning

confidence: 99%

“…First, they used convex optimization to optimize the precoder and combiner components (baseband and phase shifter) weights of the hybrid beamforming system. Then they used the exact-RBFN as the second step to get a spectral efficiency that is as close as possible to the performance of the optimal fully digital beamforming architecture [8].…”

Section: Related Workmentioning

confidence: 99%

“…Many beamforming architectures have been suggested for the 5G and beyond in the recent years [3,8,10,11,13]. Analog beamforming has been proven to provide only rough directionality when it comes to beamforming in mmWave even though it is simple and cheap to build [13].…”

Section: Introductionmentioning

confidence: 99%

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2020

IJCNC

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Beamforming for millimetre-wave (mmWave) frequencies has been studied for many years. It is considered as an important enabling technology for communications in these high-frequency ranges and it received a lot of attention in the research community. The special characteristics of the mmWave band made the beamforming problem a challenging one because it depends on many environmental and operational factors. These challenges made any model-based architecture fit only special applications, working scenarios, and specific environment geometry. All these reasons increased the need for more general machine learning based beamforming systems that can work in different environments and conditions. This increased the need for an extended adjustable dataset that can serve as a tool for any machine learning technique to build an efficient beamforming architecture. Deep MIMO dataset has been used in many architectures and designs and has proved its benefits and flexibility to fit in many cases. In this paper, we study the extension of collaborative beamforming that includes many cooperating base stations by studying the impact of User Equipment (UE) speed ranges on the beamforming performance, optimizing the parameters of the neural network architecture of the beamforming design, and suggesting the optimal design that gives the best performance for as a small dataset as possible. Suggested architecture can achieve the same performance achieved before with up to 33% reduction in the dataset size used to train the system which provides a huge reduction in the data collection and processing time.

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On the Implementation and Placement of Hybrid Beamforming for Single and Multiple Users in the Massive-MIMO MmWave Systems

Aljumaily

Hammoodi

et al. 2021

Advances in Intelligent Systems and Computing

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Machine Learning Aided Hybrid Beamforming in Massive-MIMO Millimeter Wave Systems

Cited by 15 publications

References 12 publications

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

Untitled

On the Implementation and Placement of Hybrid Beamforming for Single and Multiple Users in the Massive-MIMO MmWave Systems

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