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To accommodate the rapid change of radio propagation environment for mobile communication scenarios, millimeter-wave beamforming requires instantaneous channel state information (CSI) to update its operational parameters in real time, resulting in heavy system overhead. As the number of antennas increases, the system overhead associated with beam m...
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Context 1
... three-layer RBF-NN, as shown in Fig. 2, is adopted in the simulation owing to its prominent merits such as strong capacity in classification, fast learning convergence, and strong nonlinearity fitting ability. Certainly, the RBF-NN as a local approximation network can meet the realtime requirements, despite it is prone to the phenomenon of abnormal data when the data is ...
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... a neural network model shown in Fig. 2 is established as a beamforming weight vector classification network. Input parameters of the neural network consist of starting adjustment location, moving direction of UE. The output of the neural network is the beamforming weight vector index with the corresponding maximized beam adjustment interval. The major procedures of the ...
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... three-layer RBF-NN, as shown in Fig. 2, is adopted in the simulation owing to its prominent merits such as strong capacity in classification, fast learning convergence, and strong nonlinearity fitting ability. Certainly, the RBF-NN as a local approximation network can meet the realtime requirements, despite it is prone to the phenomenon of abnormal data when the data is ...
Context 4
... a neural network model shown in Fig. 2 is established as a beamforming weight vector classification network. Input parameters of the neural network consist of starting adjustment location, moving direction of UE. The output of the neural network is the beamforming weight vector index with the corresponding maximized beam adjustment interval. The major procedures of the ...
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Citations
... 1) Model Input and Output: An apparent task for applying an AI model for BM is defining the input and output of the AI based BM system. In [18], we attempt to reduce number of beam switching for a moving UE. The training data is collected by selecting the beam that satisfies the performance requirement while having the highest dwelling time for the moving UE. ...
Combing Artificial Intelligence (AI) and wireless communication technologies has become one of the major technologies trends towards 2030. This includes using AI to improve the efficiency of the wireless transmission and supporting AI deployment with wireless networks. In this article, the latest progress of the Third Generation Partnership Project (3GPP) standards development is introduced. Concentrating on AI model distributed transfer and AI for Beam Management (BM) with wireless network, we introduce the latest studies and explain how the existing standards should be modified to incorporate the results from academia.
... In addition to these two use cases, other minor use cases have also been proposed by various companies. For example, one may wish to predict the best beam based on a UE's location [25]. Discussions on this use case are ongoing, as the location information of a UE is considered private. ...
This paper investigates the application of artificial intelligence (AI) to wireless technology, specifically in the context of beam management (BM) in the advanced 5th-generation (5G) communication system. Our focus lies in aligning our study with the ongoing discussions within the Third Generation Partnership Project (3GPP) as of December 2022. Instead of evaluating the performance of specific AI models, we take user equipment (UE) receiver (Rx) beam prediction as an illustrative example of AI-based BM. We explore various aspects of AI model management, including model selection, monitoring, and activation/deactivation operations, from a 3GPP perspective. For model selection, we propose deploying distinct AI models for different propagation environments, categorized based on base station (BS) transmitter (Tx) beam measurement results. Reference Signal Received Power (RSRP) serves as a pivotal key performance index (KPI) for model performance monitoring. Our simulation results indicate that, instead of training one all-encompassing AI model with numerous layers for universal application, transitioning between domain-specific AI models with fewer layers yields superior performance. Model activation/deactivation procedures determine whether AI-based BM or traditional BM should be employed in a given scenario. We also introduce the use of AI for predicting the performance of both AI-based BM and traditional BM. By comparing the performance of these strategies, we can ascertain whether link performance degradation results from AI output errors or UE movement into challenging propagation environments. This approach enables the effective management of model switching between AI-based BM and traditional BM. The simulation shows that we can reduce the number of unnecessary switches by 10%. INDEX TERMS Beam management (BM), machine learning, long short-term memory (LSTM), model management, 3GPP standards.
... To achieve higher received signal power and accurately direct beams towards targets, millimeter wave (mm-wave) beamforming with massive multi-input-multi-output (MIMO) have been studied as a design methodology [3], since mm-wave offers wider bandwidth, narrower beam, and more abundant band resources [4], [5]. However, Massive MIMO systems increase implementation costs and energy consumption as each antenna requires an RF chain [6], which brings extra power consumption and complexity in hardware alignment [7]. Instead, hybrid analog digital (HAD) beamforming structure divides the beamforming process into low-dimensional digital beamforming and high-dimensional analog beamforming [8]. ...
... The communication functionality is implemented in the receiver, we apply MMSE and WMMSE of combined signal y in (3) and source signal s to extract the performance of bit error rate (BER) and spectrum efficiency, respectively [16], [17]. The definition of MSE is shown in (5), and the spectral efficiency is given by [18] in (6). ...
In this paper, an adaptive millimeter-wave (mmWave) beam tracking scheme is proposed to flexibly adjust the angular range of beam tracking based on the user-specific speeds for reducing the tracking overhead, where deep learning is exploited to accurately extract the user movement features. Specifically, long short-term memory network is utilized to predict the possible optimal beams according to the received signals of previous beam tracking. Based on the predicted probabilities, the sum-probability criterion is proposed to track the subset of maximum-probability beams whose sum-probability is larger than the predefined threshold, where the beam with the highest received power is selected as the optimal one. Considering the limited number of received beam tracking signals, a two-stage training strategy is further proposed to stabilize the model optimization. Simulation results demonstrate that our proposed scheme could effectively reduce the overhead of beam tracking in guarantee of high beamforming gains, compared with the conventional schemes.
