DeepChannel: Wireless Channel Quality Prediction Using Deep Learning

Kulkarni, Adita; Seetharam, A.; Ramesh, A.; Herath, J. Dinal

doi:10.1109/tvt.2019.2949954

Cited by 72 publications

(36 citation statements)

References 45 publications

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“…For a fair comparison, we evaluate TRADER, Oracle and Round Robin under the same network conditions, i.e., we do trace-driven simulation using as input the test sets of the LTE traces (see § II). For the number of SRS resources for each 10 ms frame we consider 64, 32…”

Section: A Methodology and Metricsmentioning

confidence: 99%

“…The literature on network state prediction at the short timescales we target is much thinner. Previous research has mostly proposed deep learning predictors that operate on time steps of seconds or less for physical layer indicators, such as bandwidth [7], transmission inactivity [31], signal strength [32], uplink throughput [8], Physical Resource Blocks (PRBs) [33], or channel state [34].Unlike these works, we are interested in user-generated traffic, and not physical layer properties.…”

Section: Related Workmentioning

confidence: 99%

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Traffic-Driven Sounding Reference Signal Resource Allocation in (Beyond) 5G Networks

Fiandrino

Attanasio

Fiore

et al. 2021

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Beyond 5G mobile networks have to support a wide range of performance requirements and unprecedented levels of flexibility. To this end, massive MIMO is a critical technology to improve spectral efficiency and thus scale up network capacity, by increasing the number of antenna elements. This also increases the overhead of Channel State Information (CSI) estimation and obtaining accurate CSI is a fundamental problem in massive MIMO systems. In this paper, we focus on scheduling uplink Sounding Reference Signals (SRSs) that carry pilot symbols for CSI estimation. Under the large number of users and high load that are expected to characterize beyond 5G systems, the limited amount of resources available for SRSs makes the legacy 3GPP periodic allocation scheme largely inefficient. We design TRADER, an SRS resource allocation framework that minimizes the age of channel estimates by taking advantage of machine learning-based short-term traffic forecasts at the base station level. By anticipating traffic bursts, TRADER schedules SRS resources so as to obtain CSI for each user right before the corresponding traffic arrives. Experiments with extensive real-world mobile network traces show that our solution is efficient and robust in high load scenarios: with respect to a round robin schedule of aperiodic SRS, TRADER provides more often CSI within the coherence time (up to 5× for given scenarios), leading to channel gains of up to 2 dB.

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Section: A Methodology and Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Traffic-Driven Sounding Reference Signal Resource Allocation in (Beyond) 5G Networks

Fiandrino

Attanasio

Fiore

et al. 2021

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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show abstract

“…Fortunately, for WLANs, the coherence time of the channel is long enough since STAs are usually stationary and the mobility is low. Also, transmission rates can be estimated with the advanced machine learning techniques [16]. We assume that the transmission rates change every T seconds, hence the Problem 1.1 is reduced to the following deterministic optimization problem as follows:…”

Section: Problemmentioning

confidence: 99%

Joint optimization of target wake time mechanism and scheduling for IEEE 802.11ax

Karaca¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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IEEE 802.11ax as the newest wireless local area networks (WLANs) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 WLAN standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called target wake time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go to sleep mode for a much longer time, and STAs with high traffic volume can have shorter TWT interval to wake up more frequently. Moreover, as multiuser transmission capability is added to 802.11ax, multiple STAs can have the same TWT interval and wake up at the same time, and hence there is a great opportunity to have collision-free transmission by scheduling multiple STAs on appreciate TWT intervals to reduce energy consumption and also increase network throughput. In this paper, we investigate the problem of STAs scheduling and TWT interval assignment together to reduce overall energy consumption of the network. We propose an algorithm that dynamically selects STAs to be served and assigns the most suitable TWT interval given STAs' traffic and channel conditions. We analyze our algorithm through Lyapunov optimization framework and show that our algorithm is arbitrarily close to the optimal performance at the price of increased queue sizes.Simulation results show that our algorithm consumes less power and support higher traffic compared to a benchmark algorithm that operates randomly for TWT assignment.

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“…Finally, a key challenge in ML methods is the need for large quantities of training data. A common theme in many prior works, such as [17], [24], [28], has been the use of ray tracing, which enables large quantities of training points to be generated via electromagnetic simulations.…”

Section: B Related Workmentioning

confidence: 99%