Introduction and Background

Enescu, Mihai; Ranta‐Aho, Karri

doi:10.1002/9781119582335.ch1

Cited by 6 publications

(16 citation statements)

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“…where B ∈ ℜ R×2 with a Gaussian kernel B ∼ N (0, s 2 I) such that the variance s 2 affect the frequency range involved in the RFF. The one-hot encoded beam sequence and RFF 1 Only a basic RNN cell is shown in the Figure 1 to represent the use of the RNN. layer output are concatenated and inputted to the RNN layer which encodes the time series data.…”

Section: B Proposed Prediction Modelmentioning

confidence: 99%

“…Here the BMR is requested for the top-k beams whose sum of the confidences scores satisfy the threshold value. This will ensure that the best beam is selected and if none of the beams in the top-k beams meet the minimum RSRP criteria [1], the conventional 5G beam selection can be utilized or skip serving at the particular time instance.…”

Section: Predictive Beam Management For 5gmentioning

confidence: 99%

“…Thus, one key drawback in such a beam-based system is the requirement of beam alignment which demands a significant effort from the communication system to maintain a reliable connection, specially when the user has mobility. 5G beam management procedures are designed effectively to monitor the link through periodic measurements to achieve a reliable connection at the expense of increased overhead and exhaustive search over the possible directions [1]. The BS or the access point (AP) transmits synchronization signal block (SSB) beams which are used for the initial access process by the UE.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the periodic beam measurement reporting is essential for maintaining the link quality, although it involves a high resource usage and overhead. Moreover, the 5G NR defines aperiodic CSI-RS triggering which allows on-demand BMR [1].…”

Section: Introductionmentioning

confidence: 99%

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LiDAR aided Wireless Networks - Beam Prediction for 5G

Marasinghe

Jayaweera

Rajatheva

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR beam management achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.

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Section: B Proposed Prediction Modelmentioning

confidence: 99%

Section: Predictive Beam Management For 5gmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LiDAR aided Wireless Networks - Beam Prediction for 5G

Marasinghe

Jayaweera

Rajatheva

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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“…The NR precoding matrix implementation for both the downlink and uplink is described in the 3GPP technical specifications (TSs) 38-211 [5] and 38-214 [6]. Moreover, several books have compiled the information of 5G-NR standardization and have exposed it more clearly [2]- [4], [7], [8]. In these references, the authors provide an overview of multi-antenna techniques with a focus on their implementation in 5G-NR.…”

Section: Introductionmentioning

confidence: 99%

Performance bounds with precoding matrices compliant with standardized 5G-NR for MIMO transmission

Villalonga

OdetAlla

García

et al. 2022

2022 IEEE Conference on Standards for Communications and Networking (CSCN)

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Advanced multiple-input multiple-output (MIMO) beamforming techniques are crucial in 5G New Radio (NR) to achieve the expected data rate values. Therefore, the 3rd Generation Partnership Project (3GPP) has proposed a codebook-based MIMO precoding strategy to provide high diversity, array gain, and spatial multiplexing. The main goal is to obtain a tradeoff between performance, signal overhead, and complexity. The precoding matrix is selected from a set of predefined codebooks based on the knowledge that the 5G-NR base station (gNB) acquires about the channel status. In this work, a detailed study of the precoding matrix design is provided following the guidelines reported in the technical specifications 38-211 and 38-214 of the 3GPP. An analysis of the performance in terms of spectral efficiency (SE) achieved by the 5G-NR precoding matrices is illustrated for a single-user MIMO scenario. These results are contrasted against the optimal singular value decomposition (SVD) solution in order to explore the gap between the standardized precoding proposal and the optimal one. Several values of signal-to-noise ratio (SNR) and different antenna array configurations are considered. Moreover, the multiplexing gain for a different number of parallel data streams is evaluated. Numerical results show the SE bounds that can be obtained with the 5G-NR precoding matrices. These insights are of key importance for practical implementation of precoding strategies in 5G-NR systems and beyond.

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UAV-Enabled Hardware-Impaired Cell-free Massive MIMO With Spatially-Correlated Rician Fading

Tentu

Amudala

Sharma

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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We study the impact of channel aging on the uplink of a cell-free (CF) massive multiple-input multiple-output (mMIMO) system by considering i) spatially-correlated Rician-faded channels; ii) hardware impairments at the access points and user equipments (UEs); and iii) two-layer large-scale fading decoding (LSFD). We first derive a closed-form spectral efficiency (SE) expression for this system, and later propose two novel optimization techniques to optimize the non-convex SE metric by exploiting the minorization-maximization (MM) method. The first one requires a numerical optimization solver, and has a high computation complexity. The second one with closed-form transmit power updates, has a trivial computation complexity. We numerically show that i) the two-layer LSFD scheme effectively mitigates the interference due to channel aging for both low-and high-velocity UEs; and ii) increasing the number of AP antennas does not mitigate the SE deterioration due to channel aging. We numerically characterize the optimal pilot length required to maximize the SE for various UE speeds. We also numerically show that the proposed closed-form MM optimization yields the same SE as that of the first technique, which requires numerical solver, and that too with a much reduced time-complexity.

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Introduction and Background

Cited by 6 publications

References 0 publications

LiDAR aided Wireless Networks - Beam Prediction for 5G

LiDAR aided Wireless Networks - Beam Prediction for 5G

Performance bounds with precoding matrices compliant with standardized 5G-NR for MIMO transmission

UAV-Enabled Hardware-Impaired Cell-free Massive MIMO With Spatially-Correlated Rician Fading

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