UAV and IRS-aided wireless communications: modeling, analysis and optimization
Bibliographic record
Abstract
With the roll-out of 5G in many countries e.g. South Korea, Canada, etc, the discussions on the development of 6G are overtaking the attention of both academia and industry. In comparison to 5G, 6G will bring a wider frequency band, higher coverage and spectral efficiency, higher data rate, low latency and more, to enhance communication for the massive number of users/devices. Two of the emerging enablers for beyond (5G) B5G and 6G communications are unmanned aerial vehicles (UAV) and intelligent reflecting surfaces (IRSs). When UAVs have already become an integral part of 5G and B5G to meet the high data rate requirement by offering better communication links due to the proactive placement at heights (i.e., flexible deployment in all the three dimensions (3D)), the IRSs have emerged as a key enabler for realizing 6G to provide better coverage by tuning the wireless environment through an intelligent reflection of the incoming signal at very little power cost. In addition, B5G networks are expected to support aerial user communications in accordance with the expanded requirements of data transmission for an aerial user. However, there are some challenges and unaddressed issues that need attention before accommodating the enablers like UAVs and IRSs in future wireless standards such as the consideration of different radio wave propagation properties between terrestrial areas and aerial areas, the effect of product path-loss experienced by IRS for both ground and aerial communication, gains associated with 3D IRS installation in static (on fixed location e.g. building height) and dynamic (on mobile UAV) environment are still unknown, and providing service to remote or un-served users on both small and large scale. In order to address the technical issues in the above context, the thesis develops few innovative enabling frameworks regarding UAV and/or IRS-assisted communication. In particular, (i) I begin with the optimal UAV-assisted data ferrying scheme to provide coverage to hard-to-reach or remote areas for delay-tolerant applications by taking aerial and ground channel model and rotary-wing UAV power consumption into account, (ii) Second, an integrated UAV-IRS scheme is proposed where IRS is installed on the UAV and therefore, can benefit from 3D deployment similar to UAV, taking important factors e.g. aerial and ground channel, height and number of IRS elements for UAV and IRS, respectively, and power consumption of both UAV and IRS in to account. Finally, (iii) to incorporate massive devices of different kinds in to consideration, I proposed large-scale IRS-assisted downlink communication for multi-BS and multi-IRS setup. For the very setup, the user association with indirect IRS-assisted communication and user association with direct BSs are studied. The impact of the fraction of each type of user, number of IRS elements and its power consumption, large-scale deployment intensity of UAV and IRS in to account. Additionally, the performance of the proposed schemes are investigated through the derivation of closed-form exact and/or accurate approximate expression of the performance metrics that include coverage/outage probability, spectral and energy efficiency, bit-error rate, etc. Finally, the provided expressions are utilized in the various optimization problem and optimization solutions are either convexified by reformulation and using approximations or by designing low complexity algorithms. The insights are drawn related to available operation modes and other critical network parameters. Finally, the results are validated through Monte-Carlo simulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".