Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication

Akram Bin Sediq; Ali Afana; Han Zhang; Melike Erol-Kantarci; Mohammad Ghassemi

REVIEW 4 major objections 4 minor 2 cited by

Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that compressing camera images with a conditional diffusion codec and feeding them to a Vision Transformer predicts future millimeter-wave link blockages with 92.78% accuracy while cutting transport bandwidth by 70.31%.

desk verdict Useful integration of known components with a plausible qualitative result, but the headline accuracy needs a clean temporal split before I would trust the numbers. read the letter →

arxiv 2501.11763 v1 pith:YC6AJRZQ submitted 2025-01-20 eess.SP

Mohammad Ghassemi , Han Zhang , Ali Afana , Akram Bin Sediq , Melike Erol-Kantarci This is my paper · ORCID

classification eess.SP

keywords VisionTransformer(ViT)Generative-AIImageCompressionDual-BandCommunicationBlockagePredictionMulti-modal6GdatasetHierarchicalFog/CloudComputingmmWave/Sub-6GHzswitching

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Millimeter-wave links drop when people or objects move between the base station and the user, so a base station that can foresee blockages can switch to the Sub-6 GHz band before the link fails. This paper asks whether the camera images needed for such foresight can be shrunk drastically before they are sent to a cloud processor, without destroying the information the predictor needs. The answer it reports is yes: a conditional-diffusion image codec cuts the transmission size by about 70%, and a Vision Transformer plus GRU trained on the reconstructed images still reaches 92.78% blockage-prediction accuracy on the DeepSense 6G scenario, above the compressed-image transformer baseline of 86.23%. The broader point is that generative compression and vision-based prediction can be combined inside a fog-cloud architecture, making vision-aided millimeter-wave reliability cheaper in bandwidth.

What carries the argument

The load-bearing object is the conditional diffusion image codec adapted from [16]: an encoder maps an image $X$ to a semantic latent variable $Z$ and texture variables, a hierarchical prior $p(Z)$ enables efficient entropy coding after quantization, and a decoder reconstructs the image from $Z$ using denoising score matching. The reconstructed images are divided into 16x16 patches, linearly embedded in 512 dimensions, combined with the received mmWave power vector, and processed by a Vision Transformer with self-attention layers; a single-layer GRU then captures temporal dependencies for binary LOS/NLOS classification. This mechanism isolates the task-relevant visual information about moving obstructions into a small latent representation so that the downstream predictor needs only a fraction of the original image bandwidth.

What would settle it

Run the same DeepSense 6G Scenario 21 experiment with a strict chronological split, training on early samples and testing on later samples while never allowing the same five-step event in both sets, and compare the accuracy; if the reported 92.78% drops noticeably, the earlier number relied on leakage rather than on the compressed images.

Watch

Extended reading notes

Core claim

The paper's central claim is that a generative image codec can be placed inside the vision-aided blockage-prediction pipeline without changing the task's outcome. Using the conditional diffusion compressor of [16], each base-station image is reduced to a quantized semantic latent variable $Z$ plus texture variables, transmitted to the cloud, and decoded by ancestral sampling; the reconstructed images are then fed, together with the received mmWave power vector, into a Vision Transformer fine-tuned from [8] and a GRU classifier. On Scenario 21 of the DeepSense 6G dataset, the system predicts whether a blockage will occur within the next five samples (about 750 ms) with 92.78% accuracy while the latent representation uses only 29.69% of the original bandwidth. The authors also report that using the uncompressed images gives 94.23%, so the compression costs only about 1.5 points of accuracy, and that their pipeline beats a transformer baseline fed the same compressed images (86.23%).

Load-bearing premise

The reported accuracy assumes that the training and test windows are cleanly separated in time, so no overlap of the same blockage event leaks between them; the paper states only a 70/20/10 split without specifying temporal separation.

Editorial extensions

If this is right

If the 92.78% accuracy holds in deployment, a base station can keep a millimeter-wave link's high data rate most of the time and switch to Sub-6 GHz only in the moments before a predicted blockage, preserving throughput during blockages.
The 70.31% bandwidth reduction means the transport link between fog node and cloud carries less than a third of the raw visual traffic, easing the signaling load in fog-cloud offloading.
The small accuracy gap between compressed (92.78%) and uncompressed (94.23%) inputs indicates that most of the visual information needed for blockage prediction survives aggressive generative compression.
Tuning the decoder noise parameter $\gamma$ from 0.5 to 0.8 improves prediction accuracy, so the reconstruction stage can be adjusted to favor task performance rather than pure perceptual quality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A reader should not treat 92.78% as a deployment number until the train/test split is verified: if overlapping five-sample windows from the same blockage event appear in both training and test sets, the accuracy would be inflated, and the paper does not specify temporal separation.
The same compression-plus-prediction pattern could plausibly extend to other sensing modalities, such as LiDAR point clouds or radar heatmaps, where a small latent must be shipped before a downstream predictor; the paper only demonstrates RGB images.
Because the diffusion decoder can produce multiple reconstructions by varying the noise level $\gamma$, the framework suggests a tunable trade-off between image fidelity and prediction accuracy that could be adapted online to changing blockage densities.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a straightforward integration of known pieces: a ViT-based blockage predictor, a conditional diffusion image codec from Yang and Mandt, and a fog-cloud offload architecture, plus a dual-band switching rule. The new part is the combination and the measurements on DeepSense 6G Scenario 21. What it does well is show that compressing images to a latent and reconstructing them with a diffusion decoder costs only a few points of accuracy compared to uncompressed images (94.23% vs 92.78%), while the transformer baseline shows a similar drop (88.12% vs 86.23%). That qualitative result is plausible and useful. The bandwidth reduction of 70.31% is a property of the codec, not the prediction pipeline, so it stands independently.

The soft spots are real, and the reader's main worry is on target. The paper never says the train/validation/test split is chronological, and with a sliding window over a continuous time series, a random split will put near-duplicates of the same blockage event in both train and test. That leaks the future into the training set and inflates the 92.78% figure. This is the load-bearing number, so it needs a clean temporal split with no overlapping windows across sets. The gamma inconsistency is also genuine: Section V-A says gamma=0.5 for all simulations, then V-B says gamma=0.8 gives the best accuracy and that 0.5 produced lower accuracy. The reported results don't say which gamma produced Table I, and if the headline number came from tuning on the test set, that is more optimism. No error bars or repeated runs are reported, which is common for this area but worth mentioning. Finally, the 0.2969 compression ratio is not fully defined relative to what baseline (JPEG, raw, something else).

The paper's limitations are correctable. The integration is legitimate, and the external dataset anchors it. The citation pattern is clean; the single self-citation [7] is peripheral. I do not see a circularity problem.

This is a limited-scope systems contribution, not a breakthrough. But it is a fair candidate for peer review: the topic fits the venue, the components are real, and the concerns are about reporting rather than the core idea. I would send it to referees, and I would ask them to require the exact data-splitting procedure and to clarify the gamma selection before accepting the accuracy claim.

Recommendation: accept for peer review with the expectation of a major revision on experimental reporting.

Referee Report

4 major / 4 minor

Summary. This manuscript proposes a vision-aided blockage prediction framework for dual-band mmWave/sub-6 GHz communication. The BS captures RGB images and mmWave received power vectors; images are compressed by a conditional diffusion codec [16] at the fog node and reconstructed in the cloud, and a ViT with a GRU head predicts whether a blockage will occur within the next r'=5 time steps. The system is evaluated on Scenario 21 of the DeepSense 6G dataset. The authors report a blockage prediction accuracy of 92.78% for the proposed method with compressed images, versus 94.23% for ViT without compression and 86.23% for a transformer baseline with compressed images, while reducing transport bandwidth by 70.31%. Throughput and BER simulations illustrate the benefit of proactive band switching.

Significance. If cleanly established, the central result is practically relevant: it would show that a compressed latent representation generated by a diffusion codec retains enough scene information for a ViT to predict mmWave blockages nearly as accurately as on uncompressed images, at a substantial transport-bandwidth saving. The use of a real-world dataset and the direct measurement of the codec's compression ratio are strengths. The paper also gives falsifiable accuracy and bandwidth claims that can be checked by re-implementation. The main qualifications are that the reported accuracy rests on a single, under-specified data split and on a possibly test-selected decoder noise parameter; both need to be resolved before the significance can be fully credited.

major comments (4)

[Section V-A and Eq. (2)] The 70/20/10 train/validation/test split is described without saying whether it is chronological or whether overlapping windows from the same blockage event are kept in one partition. Since r'=r=5 and DeepSense 6G is a continuous time series, sliding windows are heavily overlapping; a random split can place near-duplicate windows of the same blockage event in both training and test sets, letting the model memorize scene features and inflating the reported 92.78%. The authors should specify a chronological split (e.g., first 70% of each recording for training, with a gap before the test segment) or group windows by event, and report the resulting accuracy. This is the load-bearing point for the headline claim.
[Section V-A vs. Section V-B / Fig. 5] Section V-A states that 'the noise level parameter γ was set to 0.5 for all simulations,' but Section V-B reports in the text and Fig. 5 that γ=0.8 gives the best accuracy. If the 92.78% figure was obtained by first observing the effect of γ on test accuracy, the reported number is an optimistically selected result. The authors must clarify whether γ was chosen on the validation set before evaluating the test set, and should report accuracy for the γ values shown in Fig. 5.
[Table I, Section V-B] Table I reports single accuracy numbers for each method, with no error bars, multiple seeds, or statistical tests. Given that ViT and the transformer baseline differ by only 6.55 percentage points (92.78% vs. 86.23%), the claim that the proposed method 'outperforms' the baseline is not supported without a measure of variance or a significance test. The authors should train each configuration over several seeds with the same split and report mean and standard deviation, or at least a confidence interval.
[Section V-B, bandwidth reduction] The 70.31% bandwidth reduction is stated as following from a compression ratio of 0.2969 on the latent z, but the baseline is not explicit: if the input to the diffusion codec is itself JPEG-compressed, the reduction is relative to JPEG images, not to raw sensor images. Please state the baseline bitstream (raw, JPEG, or other) and include the size of the latent after entropy coding; without these details the headline bandwidth claim is not quantitatively verifiable.

minor comments (4)

[Section IV-A, Eq. (4)] The notation in Eq. (4) uses both X and Y for the image: the text defines X0=X, and Eq. (4) writes p(X0:N, Z)=p(Y|Z)p(Z). The reconstruction target should be named consistently.
[Section V-A] The sentence 'The BS and the user are placed d=10.6 meters apart, facing opposite directions' is ambiguous; please clarify whether this is a physical setup description of DeepSense Scenario 21 or a simulation assumption, and how camera coverage is affected.
[Figure 3] The compressed image is shown at a small scale; include quantitative distortion metrics such as PSNR or MS-SSIM for the reconstructed images to substantiate the claim that the visual degradation has minimal impact on prediction accuracy.
[Section IV-A] The paper does not specify which checkpoint or rate point of the diffusion codec [16] was used; adding this implementation detail would improve reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No circularity: all load-bearing components (compression codec, ViT pretraining, dataset) are external; the only self-citation is peripheral.

full rationale

The paper's derivation chain is benchmarked externally at every load-bearing step. The blockage predictor (ViT plus GRU) is trained and evaluated on the DeepSense 6G Scenario 21 dataset with a 70/20/10 split and balanced LOS/NLOS sequences; no parameter is fit to the reported accuracy and then renamed as a prediction. The compression codec is adopted from [16] (Yang and Mandt), an external prior work, and the average compression ratio 0.2969 is a measured property of that codec's latent representation, not a fitted constant. ViT initialization comes from [8] (Dosovitskiy), also external. The only self-citation, [7], appears in the introduction as an example of sensing-capable base stations and does not carry the central argument. The concerns about an unspecified temporal split and the gamma inconsistency in Sections V-A and V-B are experimental validity and correctness risks, not circularity; they do not make the reported result equivalent to an input by construction. No circular step can be exhibited, so no specific circularity is claimed.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No new physical entities are introduced. The system borrows the diffusion codec [16], pretrained ViT [8], and DeepSense data. The only hand-tuned parameter is the decoder noise level gamma; the 70.31% bandwidth figure depends on the latent-to-image bit ratio from [16], whose computation is not shown.

free parameters (1)

Diffusion decoder noise level gamma = 0.5 in main runs; 0.8 recommended after tuning
Controls stochastic reconstruction in the conditional diffusion decoder; the authors set 0.5, then report in Fig.5 that 0.8 gives the best blockage prediction accuracy, without specifying a held-out validation set.

assumptions (3)

domain assumption DeepSense 6G Scenario 21 labels and sensor streams are accurate ground truth for LOS/NLOS and are synchronized with images.
The entire evaluation uses this public dataset as ground truth; any label or synchronization error transfers to the reported accuracy. Section IV-C.
domain assumption The fog-cloud transport link is noise-free, so compression and reconstruction are the only degradations.
Stated in Section III-C: 'we assume the communication link between the fog node and the cloud center is noise-free.'
domain assumption The diffusion codec from [16] can be used as a black box, and its reconstructed images retain the visual cues needed for blockage prediction.
The paper relies on a pretrained external codec; the central claim depends on this preservation. Section IV-A.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication." pith.science (2026). https://pith.science/paper/YC6AJRZQ

@misc{pith2026250111763,
  author       = {Pith},
  title        = {Pith review of: Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YC6AJRZQ}},
  note         = {Machine review of arXiv:2501.11763}
}

read the original abstract

In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual data has been leveraged to enhance blockage prediction accuracy in mmWave networks. In this work, we propose a Vision Transformer (ViT)-based approach for visual-aided blockage prediction that intelligently switches between mmWave and Sub-6 GHz frequencies to maximize network throughput and maintain reliable connectivity. Given the computational demands of processing visual data, we implement our solution within a hierarchical fog-cloud computing architecture, where fog nodes collaborate with cloud servers to efficiently manage computational tasks. This structure incorporates a generative AI-based compression technique that significantly reduces the volume of visual data transmitted between fog nodes and cloud centers. Our proposed method is tested with the real-world DeepSense 6G dataset, and according to the simulation results, it achieves a blockage prediction accuracy of 92.78% while reducing bandwidth usage by 70.31%.

Figures

Figures reproduced from arXiv: 2501.11763 by the authors.

**Figure 2.** Visual-aided blockage prediction framework at the fog-cloud system, processed through an encoder-decoder architecture and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Image compression comparison. to Pt = 40 dBm, and the background noise power is set to N0 = -10 dBm. hk[t] is influenced by two key factors at t. The first factor is frequency f, which is impacted by the free-space path loss model expressed as P LLOS = 20 log10(d)+20 log10(f)+20 log10 4π c , where d denotes the distance between transmitter and receiver and c denotes the speed of light. The second factor is blockag… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Loss value comparison of blockage prediction methods. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: Comparison of cumulative network throughput over time. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: Comparison of cumulative network throughput under [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: BER Comparison under varying numbers of blockages [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G
eess.SP 2025-06 reject novelty 6.0 of 10

A conditional diffusion model conditioned on sensing channel estimates and user location is claimed to improve uplink channel estimation in cell-free ISAC, with simulated NMSE gains over LS and MMSE.
Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
eess.SP 2025-06 reject novelty 4.0 of 10

A fine-tuned wireless foundation model provides channel embeddings that feed a DDPG agent, which reportedly improves spectral efficiency over DRL with raw CSI and over beam sweeping in DeepMIMO simulation.

Reference graph

Works this paper leans on

21 extracted references · 20 canonical work pages · cited by 2 Pith papers

[7]

Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer

M. Farzanullah, H. Zhang, A. B. Sediq, A. Afana, and M. Erol- Kantarci, “Generative ai empowered lidar point cloud generation with multimodal transformer,” arXiv preprint arXiv:2406.18542 , 2024

work page Pith review arXiv 2024
[16]

Lossy image compression with conditional diffusion models,

R. Yang and S. Mandt, “Lossy image compression with conditional diffusion models,” Advances in Neural Information Processing Sys- tems, vol. 36, 2024

work page 2024
[1]

Wireless com- munications and applications above 100 ghz: Opportunities and challenges for 6g and beyond,

T. S. Rappaport, Y . Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless com- munications and applications above 100 ghz: Opportunities and challenges for 6g and beyond,” IEEE access , vol. 7, pp. 78 729– 78 757, 2019

work page 2019
[2]

Computer vision aided blockage prediction in real-world millimeter wave deployments,

G. Charan and A. Alkhateeb, “Computer vision aided blockage prediction in real-world millimeter wave deployments,” 2022 IEEE Globecom Workshops (GC Wkshps) , pp. 1711–1716, 2022

work page 2022
[3]

Radio resource and beam management in 5g mmwave using clustering and deep reinforcement learning,

M. Elsayed and M. Erol-Kantarci, “Radio resource and beam management in 5g mmwave using clustering and deep reinforcement learning,” GLOBECOM 2020-2020 IEEE Global Communications Conference, pp. 1–6, 2020

work page 2020
[4]

Integrated mil- limeter wave and sub-6 ghz wireless networks: A roadmap for joint mobile broadband and ultra-reliable low-latency communications,

O. Semiari, W. Saad, M. Bennis, and M. Debbah, “Integrated mil- limeter wave and sub-6 ghz wireless networks: A roadmap for joint mobile broadband and ultra-reliable low-latency communications,” IEEE Wireless Communications , vol. 26, no. 2, pp. 109–115, 2019

work page 2019
[5]

Communication-efficient framework for distributed image semantic wireless transmission,

B. Xie, Y . Wu, Y . Shi, D. W. K. Ng, and W. Zhang, “Communication-efficient framework for distributed image semantic wireless transmission,” IEEE Internet of Things Journal , 2023

work page 2023
[6]

Semantic-aware federated blockage prediction (sfbp) in vision-aided next-generation wireless network,

A. R. Khan, H. U. Manzoor, R. N. B. Rais, S. Hussain, L. Mohjazi, M. A. Imran, and A. Zoha, “Semantic-aware federated blockage prediction (sfbp) in vision-aided next-generation wireless network,” Authorea Preprints, 2024

work page 2024

Show all 21 references

[8]

An image is worth 16x16 words: Transformers for image recognition at scale,

A. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020

2010 arXiv
[9]

Vit los v2x: Vision transformers for environment-aware los blockage prediction for 6g vehicular networks,

G. Gharsallah and G. Kaddoum, “Vit los v2x: Vision transformers for environment-aware los blockage prediction for 6g vehicular networks,” IEEE Access , 2024

2024
[10]

Optimized backhaul compression for uplink cloud radio access network,

Y . Zhou and W. Yu, “Optimized backhaul compression for uplink cloud radio access network,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1295–1307, 2014

2014
[11]

Computation offloading in cloud-ran based mobile cloud computing system,

J. Cheng, Y . Shi, B. Bai, and W. Chen, “Computation offloading in cloud-ran based mobile cloud computing system,” 2016 IEEE International Conference on Communications (ICC) , pp. 1–6, 2016

2016
[12]

Joint data compression and computation offloading in hierarchical fog-cloud systems,

T. T. Nguyen, V . N. Ha, L. B. Le, and R. Schober, “Joint data compression and computation offloading in hierarchical fog-cloud systems,” IEEE Transactions on Wireless Communications , vol. 19, no. 1, pp. 293–309, 2019

2019
[13]

Cooperative fog-cloud computing enhanced by full-duplex communications,

M. Liu, Y . Mao, and S. Leng, “Cooperative fog-cloud computing enhanced by full-duplex communications,” IEEE Communications Letters, vol. 22, no. 10, pp. 2044–2047, 2018

2018
[14]

Visual network index cisco. forecast and methodology

Cisco, “Visual network index cisco. forecast and methodology.” White Paper, 2017

2017
[15]

Multimedia recommendation and transmis- sion system based on cloud platform,

J. Yang, H. Wang, Z. Lv, W. Wei, H. Song, M. Erol-Kantarci, B. Kantarci, and S. He, “Multimedia recommendation and transmis- sion system based on cloud platform,” Future Generation Computer Systems, vol. 70, pp. 94–103, 2017

2017
[17]

Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction,

M. Alrabeiah, A. Hredzak, and A. Alkhateeb, “Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction,” 2020 IEEE 91st vehicular technology conference (VTC2020-Spring) , pp. 1–5, 2020

2020
[18]

Deep learning for mmwave beam and blockage prediction using sub-6 ghz channels,

M. Alrabeiah and A. Alkhateeb, “Deep learning for mmwave beam and blockage prediction using sub-6 ghz channels,” IEEE Transactions on Communications , vol. 68, no. 9, pp. 5504–5518, 2020

2020
[19]

Fog computing: Principles, architectures, and applica- tions,

A. V . Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya, “Fog computing: Principles, architectures, and applica- tions,” Elsevier, pp. 61–75, 2016

2016
[20]

High-fidelity generative image compression,

F. Mentzer, G. D. Toderici, M. Tschannen, and E. Agustsson, “High-fidelity generative image compression,” Advances in Neural Information Processing Systems , vol. 33, pp. 11 913–11 924, 2020

2020
[21]

Study on channel model for frequencies from 0.5 to 100 ghz,

3GPP, “Study on channel model for frequencies from 0.5 to 100 ghz,” 3rd Generation Partnership Project (3GPP), Technical Report TR 38.901, v18.0.0 , 2024

2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [7]

Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer

M. Farzanullah, H. Zhang, A. B. Sediq, A. Afana, and M. Erol- Kantarci, “Generative ai empowered lidar point cloud generation with multimodal transformer,” arXiv preprint arXiv:2406.18542 , 2024

work page Pith review arXiv 2024

[2] [16]

Lossy image compression with conditional diffusion models,

R. Yang and S. Mandt, “Lossy image compression with conditional diffusion models,” Advances in Neural Information Processing Sys- tems, vol. 36, 2024

work page 2024

[3] [1]

Wireless com- munications and applications above 100 ghz: Opportunities and challenges for 6g and beyond,

T. S. Rappaport, Y . Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless com- munications and applications above 100 ghz: Opportunities and challenges for 6g and beyond,” IEEE access , vol. 7, pp. 78 729– 78 757, 2019

work page 2019

[4] [2]

Computer vision aided blockage prediction in real-world millimeter wave deployments,

G. Charan and A. Alkhateeb, “Computer vision aided blockage prediction in real-world millimeter wave deployments,” 2022 IEEE Globecom Workshops (GC Wkshps) , pp. 1711–1716, 2022

work page 2022

[5] [3]

Radio resource and beam management in 5g mmwave using clustering and deep reinforcement learning,

M. Elsayed and M. Erol-Kantarci, “Radio resource and beam management in 5g mmwave using clustering and deep reinforcement learning,” GLOBECOM 2020-2020 IEEE Global Communications Conference, pp. 1–6, 2020

work page 2020

[6] [4]

Integrated mil- limeter wave and sub-6 ghz wireless networks: A roadmap for joint mobile broadband and ultra-reliable low-latency communications,

O. Semiari, W. Saad, M. Bennis, and M. Debbah, “Integrated mil- limeter wave and sub-6 ghz wireless networks: A roadmap for joint mobile broadband and ultra-reliable low-latency communications,” IEEE Wireless Communications , vol. 26, no. 2, pp. 109–115, 2019

work page 2019

[7] [5]

Communication-efficient framework for distributed image semantic wireless transmission,

B. Xie, Y . Wu, Y . Shi, D. W. K. Ng, and W. Zhang, “Communication-efficient framework for distributed image semantic wireless transmission,” IEEE Internet of Things Journal , 2023

work page 2023

[8] [6]

Semantic-aware federated blockage prediction (sfbp) in vision-aided next-generation wireless network,

A. R. Khan, H. U. Manzoor, R. N. B. Rais, S. Hussain, L. Mohjazi, M. A. Imran, and A. Zoha, “Semantic-aware federated blockage prediction (sfbp) in vision-aided next-generation wireless network,” Authorea Preprints, 2024

work page 2024