SDR-based Testbed for Real-time CQI Prediction for URLLC

Glinskiy, Kirill; Kureev, Aleksey; Khorov, Evgeny

doi:10.1109/infocomwkshps51825.2021.9484554

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…• The testbed has to be compatible with off-the-shelf cellular base stations to obtain measurements for reallife scenarios without the need to deploy cutting-edge equipment. The testbed and the overall method used in the datasetgathering process were originally proposed in [35]. How- ever, in this paper, they are extended to accommodate different frequency bands and bandwidths.…”

Section: B Experimental Testbedmentioning

confidence: 99%

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ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecondlevel delays and very high reliability, i.e., less than 10 −5 packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.

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Section: B Experimental Testbedmentioning

confidence: 99%

“…In the experiment, we record the channel state as Inphase and Quadrature (IQ) samples and process them using a modified version of the srsLTE software suite [35] in realtime. The output of the data processing pipeline is the CQI and SINR values for each RB.…”

Section: B Experimental Testbedmentioning

confidence: 99%

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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“…Some papers, such as [11] model the channel state as a point on a certain manifold and extrapolate the channel by following a line on the created manifold from previous measurements. Recently, machine learning (ML) algorithms have been proposed, including those based on neural networks with different architectures [12][13][14][15][16][17][18][19]. Generally, the channel prediction task is formulated as a time series prediction problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, the channel prediction task is formulated as a time series prediction problem. The authors use a variety of neural network architectures suitable for time series: recurrent neural networks for CSI prediction [12,13], convolutional neural networks [15], and combinations of both [14,16]. The paper [17], on the other hand, formulates the task as a reinforcement learning task, employing Q-learning for optimal beamforming.…”

Section: Literature Reviewmentioning

confidence: 99%

PABAFT: Channel Prediction Approach Based on Autoregression and Flexible TDD for 5G Systems

et al. 2022

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To achieve high gains from multiple antennas in 5G systems, the base station (gNB) constructs precoders using channel measurements obtained based on pilot signals. For high user mobility, the measurements quickly become outdated, which is especially crucial for ultra-reliable low latency communications (URLLC) traffic because it increases channel resource consumption to provide highly reliable transmissions and, consequently, reduces system capacity. Frequent pilot transmissions can provide accurate channel estimation and high-quality precoder but lead to huge overhead. Fortunately, 5G systems enable flexible time division duplex (TDD), which allows the gNB to dynamically change the configuration of downlink and uplink slots and tune the period of channel measurements. The paper exploits this feature and designs a new prediction approach based on autoregression and flexible TDD (PABAFT) that forecasts the channel between consequent pilots transmissions. To learn fine-grained channel properties, the gNB configures uplink pilot transmission in each slot. When the training data are collected, and the model is fitted, the gNB switches back to the regular slot configuration with a long pilot transmission period. Extensive simulations with NS-3 in high-mobility scenarios show that PABAFT provides the signal-to-noise ratio (SNR) close to that with the ideal knowledge of the channel at the gNB. In addition, PABAFT significantly reduces channel resource consumption and, thus, increases capacity for URLLC traffic in comparison to the existing solutions.

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CQI Prediction Through Recurrent Neural Network for UAV Control Information Exchange Under URLLC Regime

Bartoli

Marabissi

2022

IEEE Trans. Veh. Technol.

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SDR-based Testbed for Real-time CQI Prediction for URLLC

Cited by 3 publications

References 5 publications

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

PABAFT: Channel Prediction Approach Based on Autoregression and Flexible TDD for 5G Systems

CQI Prediction Through Recurrent Neural Network for UAV Control Information Exchange Under URLLC Regime

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