Measurement-Based Channel Characterization for 5G Downlink Based on Passive Sounding in Sub-6 GHz 5G Commercial Networks

Wu, Tianqi; Yin, Xuefeng; Zhang, Linjian; Ning, Jie

doi:10.1109/twc.2020.3048358

Cited by 20 publications

(16 citation statements)

References 29 publications

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“…The outrage probability of the wireless network under various RIS channel conditions is analyzed. A downlink channel characterization scheme of sub-6GHz wireless network is studied in [122]. This measurement-based channel characterization utilizes EM algorithm for multi-dimensional channel parameter estimation.…”

Section: ) Expectation Maximization (Em)mentioning

confidence: 99%

“…Research topic Research analysis/findings K-means clustering [103] Joint resource allocation and clustering mechanisms Energy efficiency analysis [104] Routing protocol with K-means clustering, maximum stable set problem and continuous hopfield network Improved throughput, transmission delay reduction and cluster stability [105] Routing algorithm Data transmission rate analysis and energy utilization [106] Edge computing node deployment mechanism Analysis of computing resources deployment cost and network delay tradeoff with proposed mechanism and comparison of that against traditional K-means clustering and random deployment method [107] Non-linear equalization operation Performance evaluation in terms of computational complexity, cost and hardware constraints [108] Device clustering scheme Improved packet delivery ratio and latency compared to ACO and GWO [109] Unequal clustering mechanism Transmission delay and energy consumption reduction with proposed algorithm and comparison of that against withEKMT, UCR and CU-CRA" [110] Device clustering scheme Comparison of energy utilization against low-energy adaptive clustering hierarchy (LEACH) based approaches [111] Device clustering scheme Analysis of energy consumption balancing with the proposed scheme [112] Device clustering scheme Analysis of network coverage and energy utilization tradeoff [113] Device clustering scheme Analysis of transmission delay reduction and protection from malicious device induced attacks [114] Data clustering Immunity from DoS attacks Expectation Maximization [115] Aerial base station deployment with proposed EM based approach Improved downlink capacity, low energy consumption and service delay against traditional EM and K-means approach [116] Indoor localization Performance evaluation of the localization method [117] Indoor localization Performance evaluation against KNN in terms of localization errors [118] Time difference of arrival (TDOA) source direct position determination Reduction of computational load of the direct position determination [119] Passive localization scheme Analysis of the localization performance, computational complexity and communication overhead [120] Mulltitarget localization scheme Localization error analysis with the proposed method, basis pursuit, GMP and least square compressive sensing [121] RIS channel modeling Outrage probability analysis for the proposed approach [122] Channel characterization method Performance analysis in terms of RMS delay, AOA and ZOA spread [123...…”

Section: Algorithms Referencesmentioning

confidence: 99%

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A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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Section: ) Expectation Maximization (Em)mentioning

confidence: 99%

Section: Algorithms Referencesmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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“…Massive multiple-input multiple-output (MIMO) has become the key technology of the fifth generation (5G) wireless communication system, due to its advantages in system capacity and link robustness [ 1 , 2 ], etc. As premises of these advantages, the base station (BS) needs to obtain accurate downlink channel state information (CSI), and rely on downlink CSI for precoding [ 3 ], antenna selection [ 4 ], radio resource allocation [ 5 ], and communication interference management [ 6 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for 1-bit compressed sensing-based superimposed CSI feedback

Qing

Cai

et al. 2022

PLoS ONE

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In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and large processing delays. To overcome these drawbacks, this paper proposes a deep learning (DL) scheme to improve the 1-bit compressed sensing-based superimposed CSI feedback. On the user side, the downlink CSI is compressed with the 1-bit CS technique, superimposed on the uplink user data sequences (UL-US), and then sent back to the base station (BS). At the BS, based on the model-driven approach and assisted by the superimposition-interference cancellation technology, a multi-task detection network is first constructed for detecting both the UL-US and downlink CSI. In particular, this detection network is jointly trained to detect the UL-US and downlink CSI simultaneously, capturing a globally optimized network parameter. Then, with the recovered bits for the downlink CSI, a lightweight reconstruction scheme, which consists of an initial feature extraction of the downlink CSI with the simplified traditional method and a single hidden layer network, is utilized to reconstruct the downlink CSI with low processing delay. Compared with the 1-bit CS-based superimposed CSI feedback scheme, the proposed scheme improves the recovery accuracy of the UL-US and downlink CSI with lower processing delay and possesses robustness against parameter variations.

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“…Direction of arrival (DoA) estimation has been the subject of much recent research, particularly in the context of channel characterization for wireless communications [1,2]. Conventional receiver architectures for DoA estimation leverage antenna array-based solutions with a dedicated signal processing layer, such as MUSIC [3], ESPRIT [4], and SAGE [5]. Antenna arrays typically have elements separated by λ/2 where λ is free-space wavelength.…”

Section: Introductionmentioning

confidence: 99%

Graph Attention Network-Based Single-Pixel Compressive Direction of Arrival Estimation

Tekbıyık¹,

Yurduseven

Kurt

2022

IEEE Commun. Lett.

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In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a codedaperture technique, probing the spectrum of far-field sources that are incident on the aperture using a set of spatio-temporally incoherent modes. This information is then encoded and compressed into the channel of the coded-aperture. The coded-aperture is based on a metasurface antenna design and it works as a receiver, exhibiting a single-channel and replacing the conventional multichannel raster scan-based solutions for DoA estimation. The GAT network enables the compressive DoA estimation framework to learn the DoA information directly from the measurements acquired using the coded-aperture. This step eliminates the need for an additional reconstruction step and significantly simplifies the processing layer to achieve DoA estimation. We show that the presented GAT integrated single-pixel radar framework can retrieve high fidelity DoA information even under relatively low signal-to-noise ratio (SNR) levels.

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Measurement-Based Channel Characterization for 5G Downlink Based on Passive Sounding in Sub-6 GHz 5G Commercial Networks

Cited by 20 publications

References 29 publications

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

Deep learning for 1-bit compressed sensing-based superimposed CSI feedback

Graph Attention Network-Based Single-Pixel Compressive Direction of Arrival Estimation

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