The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split learning (MultSL), in which a large neural network (NN) is split into two wirelessly connected segments. The upper segment combines images and received powers for future received power prediction, whereas the lower segment extracts features from camera images and compresses its output to reduce communication costs and privacy leakage. Experimental evaluation corroborates that MultSL achieves higher accuracy than the baselines utilizing either images or RF signals. Remarkably, without compromising accuracy, compressing the lower segment output by 16x yields 16x lower communication latency and 2.8% less privacy leakage compared to the case without compression.
This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks. Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the cam…
Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural network architecture that compresses the communication payload, i.e., images. Compared to a baseline solely utilizing RF signals, numerical results show that SL integrating only one pixel image with RF signals achieves higher prediction accuracy whi…
Beamforming is the primary technology to overcome the high path loss in millimeter-wave (mmWave) channels. Hence, performance improvement needs knowledge and control of the spatial domain. In particular, antenna structure and radiation parameters affect the beamforming performance in mmWave communications systems. In order to address the impairments such as beam misalignments, outage loss, tracking inability, blockage, etc., an optimum value of the beamwidth must be determined. In our previous paper, assuming a communication system that creates a beam per cluster, we theoretically investigated…
The goal of this work is the accurate prediction of millimeter-wave received power leveraging both radio frequency (RF) signals and heterogeneous visual data from multiple distributed cameras, in a communication and energy-efficient manner while preserving data privacy. To this end, firstly focusing on data privacy, we propose heteromodal split learning with feature aggregation (HetSLAgg) that splits neural network (NN) models into camera-side and base station (BS)-side segments. The BS-side NN segment fuses RF signals and uploaded image features without collecting raw images. However, the usa…
For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance---e.g., the cumulative sum of time-varying data rates---proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proac…
For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that directly maps images to a handover decision. In the framework, camera images are utilized to allow for the prediction of blockage effects in a mmWave link, whereby a network controller triggers a handover in a proactive fashion. Furthermore, direct mapping allows for the scalability of the number of pedestrians. This paper experimentally investigates the feasibility of adopting cooperative sensing with multiple cameras tha…
The conventional outage in wireless communication systems is caused by the deterioration of the wireless communication link, i.e., the received signal power is less than the minimum received signal power. Is there a possibility that the outage occurs in wireless communication systems with a good channel state? Based on both communication and heat transfer theories, a power-consumption outage in the wireless communication between millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) base stations (BSs) and smartphones has been modeled and analyzed. Moreover, the total transmiss…
The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs) and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the n…
In this letter, we focus on the hybrid precoding (HP) design for the reconfigurable intelligent surface (RIS) aided multi-user (MU) millimeter wave (mmWave) communication systems. Specifically, we aim to minimize the mean-squared-error (MSE) between the received symbols and the transmitted symbols by jointly optimizing the analog-digital HP at the base-station (BS) and the phase shifts (PSs) at the RIS, where the non-convex element-wise constant-modulus constraints for the analog precoding and the PSs are tackled by resorting to the gradient-projection (GP) method. We analytically prove the co…
Millimeter wave (mmWave) technology is expected to dominate the future 5G networks mainly due to large spectrum available at these frequencies. However, coverage deteriorates significantly at mmWave frequencies due to higher path loss, especially for the non-line-of-sight (NLOS) scenarios. In this work, we explore the use of passive reflectors for improving mmWave signal coverage in NLOS indoor areas. Measurements are carried out using the PXI-based mmWave transceiver platforms from National Instruments operating at 28 GHz, and the results are compared with the outcomes of ray tracing (RT) sim…
Virginia Tech（弗吉尼亚理工大学）；Intelligent Automation, Inc.（智能自动化公司）
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By utilizing a subset of beams in the IA process, DeepIA removes the need for an exhaustive beam search thereby reducing the beam sweep time in IA. A deep neural network (DNN) is trained to learn the complex mapping from the received signal strengths (RSSs) collected with a reduced number of beams to the optimal spatial beam of the receiver (among a larger set of beams). In test time, DeepIA measures RSSs only from a sma…
In millimeter-wave (mmWave) channels, to overcome the high path loss, beamforming is required. Hence, the spatial representation of the channel is essential. Further, for accurate beam alignment and minimizing the outages, inter-beam interferences, etc., cluster-level spatial modeling is also necessary. Since, statistical channel models fail to reproduce the intra-cluster parameters due to the site-specific nature of the mmWave channel, in this paper, we propose a ray tracing intra-cluster model (RT-ICM) for mmWave channels. The model considers only the first-order reflection; thereby reducing…
University of Maryland（马里兰大学）；Intelligent Automation, Inc.（智能自动化公司）
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications. To establish the IA between the base station (e.g., gNodeB) and user equipment (UE) for directional transmissions, a deep neural network (DNN) can predict the beam that is best slanted to each UE by using the received signal strengths (RSSs) from a subset of possible narrow beams. While improving the latency and reliability of beam selection compared to the conventional IA that sweeps all beams, the DNN itself is sus…
Virginia Tech（弗吉尼亚理工大学）；Intelligent Automation Inc.（智能自动化公司）
We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA's beam prediction accuracy. …
Rutgers, The State University of New Jersey（罗格斯新泽西州立大学）；University of Pennsylvania（宾夕法尼亚大学）
While millimeter wave (mmWave) communications promise high data rates, their sensitivity to blockage and severe signal attenuation presents challenges in their deployment in urban settings. To overcome these effects, we consider a distributed cooperative beamforming system, which relies on static relays deployed in clusters with similar channel characteristics, and where, at every time instance, only one relay from each cluster is selected to participate in beamforming to the destination. To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay sele…
