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In this paper, we delve into the domain of heterogeneous drone-enabled aerial base stations, each equipped with varying transmit powers, serving as downlink wireless providers for ground users. A central challenge lies in strategically selecting and deploying a subset from the available drone base stations (DBSs) to meet the downlink data rate requirements while minimizing the overall power consumption. To tackle this, we formulate an optimization problem to identify the optimal subset of DBSs, ensuring wireless coverage with an acceptable transmission rate in the downlink path. Moreover, we determine their 3D positions for power consumption optimization. Assuming DBSs operate within the same frequency band, we introduce an innovative, computationally efficient beamforming method to mitigate intercell interference in the downlink. We propose a Kalai–Smorodinsky bargaining solution to establish the optimal beamforming strategy, compensating for interference-related impairments. Our simulation results underscore the efficacy of our solution and offer valuable insights into the performance intricacies of heterogeneous drone-based small-cell networks.
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An Exploration of the Heterogeneous Unsourced MAC
The unsourced multiple access channel (MAC) model was originally introduced to study the communication scenario in which a number of devices with low-complexity and low-energy wish to upload their respective messages to a base station. In the original problem formulation, all devices communicate using the same information rate: this may be very inefficient in certain wireless situations with varied channel conditions, power budgets, and payload requirements at the devices. This paper extends the original problem setting to allow for heterogeneous transmissions. More specifically, we consider the scenario in which devices are clustered into two classes with different signal-to-noise ratio (SNR) levels and payload requirements. In the cluster with higher power, devices transmit using a two-layer superposition modulation. In the cluster with lower energy, users transmit with the same base constellation as in the high power cluster. Within each layer, devices employ the same codebook. At the receiver, signal groupings are recovered using Coded Compressed Sensing (CCS) decoder. An outer code is further employed to stitch fragments together across times and layers, as needed. This pragmatic approach to heterogeneous CCS is validated numerically and design guidelines are identified.
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- Award ID(s):
- 2131106
- PAR ID:
- 10617901
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-5828-3
- Page Range / eLocation ID:
- 954 to 958
- Format(s):
- Medium: X
- Location:
- Pacific Grove, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF53345.2021.9723339
