SNR-CQI Mapping for 5G Downlink Network

Thyagarajan, Abitha K.; Balasubramanian, Priyeash; Vydeki, D.; Karthik, M.

doi:10.1109/apwimob51111.2021.9435258

Cited by 15 publications

(2 citation statements)

References 5 publications

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“…The best and worst SNR values considered are -5 dB and +25 dB accordingly (as done in [11]). Then the theoretical channel capacity is between 17.1 Mbps and 359 Mbps accordingly for given bandwidth of 43.2 MHz.…”

Section: A Numerical Resultsmentioning

confidence: 99%

The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

Kulmar

Müürsepp

Alam

2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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Network slicing in 5G RAN enables building logical networks on top of physical infrastructure. In case of RAN spectrum resources are divided into small bandwidth parts then, the network function in core network, namely Network Slice Admission Control Function (NSACF) enables to limit the count of user equipments (UE) and packet data unit (PDU) sessions in a slice. This paper presents negative results and a practical analysis of how many UEs and PDU sessions can be allowed to use if available bandwidth is fixed and tries to evaluate the optimal count of subslices at this bandwidth. In case of slice resource overutilization there are 2 options: increase resources or move UEs to another slice. In this paper, the feasibility of the other option is evaluated in case of fixed overall bandwidth where resource increase is not possible. Results show that infrastructure can afford as few slices as possible and slicing does not help if theoretical capacity is exceeded.

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Section: A Numerical Resultsmentioning

confidence: 99%

The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

Kulmar

Müürsepp

Alam

2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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“…Therefore, PEACH can pave the way towards implementation of proactive and intelligent AMC algorithms that could enable more securely aggressive MCS selections in advance. Such a novel AMC algorithm via PEACH could provide substantial gain with intelligent MCS selection where even better SNR-CQI mapping for MCS selection is shown to provide improved network performance in terms of throughput gain up to 30% [62]. Using PEACH in AMC algorithms is deferred to future work.…”

Section: Potential Use Casesmentioning

confidence: 99%

PEACH: Proactive and Environment Aware Channel State Information Prediction with Depth Images

Ayvasik

Mehmeti

Babaians

et al. 2023

SIGMETRICS Perform. Eval. Rev.

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Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation, in ideal conditions without interference, our experimental results show that PEACH yields the similar performance in terms of average bit error rate. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation when compared to traditional approaches.

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5G new radio multiuser precoding in geostationary satellite networks

Artiga,

Vázquez

2024

Satell Commun Network

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SummaryThe multiuser precoding procedures standardized by the 3rd Generation Partnership Project (3GPP) for new radio (NR) represent a new opportunity to realize satellite precoding for capacity improvement, given the integration of non‐terrestrial networks in the 3GPP framework. This work analyses and numerically evaluates them for the geostationary satellite scenarios consolidated by the 3GPP. It shows that the preferred precoding matrix indicated by NR codebooks is not able to handle properly the inter‐beam interference and should only be used to estimate the propagation channel. To this aim, we propose the use of modified type II port selection codebooks reporting on the strongest beams, which correspond to the higher interferences. Since the obtention of high precoding gains requires the operation in rather high signal‐to‐noise regimes and the proper estimation of the interfering contributions from many beams, we propose to enlarge the maximum number of beams reported by each precoding matrix indicator (PMI) report, currently limited to four. As alternative, we also evaluate a sequential multi‐report framework considering the effects of channel‐state information aging and the user equipment (UE) processing times. It preserves the current limits on the number of reported beams but increases the system overhead signalling. The quantization of estimated coefficients is also analysed, not only from the overhead point of view but also to properly report weak interferers corresponding to distant beams, due to the increase in the number of reported beams. Moreover, we identify the minimum interference power a UE should be able to detect to achieve the theoretical large precoding gains.

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SNR-CQI Mapping for 5G Downlink Network

Cited by 15 publications

References 5 publications

The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

PEACH: Proactive and Environment Aware Channel State Information Prediction with Depth Images

5G new radio multiuser precoding in geostationary satellite networks

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