| 1. |
- Pereira, Andreia, et al.
(author)
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A Low Complexity Sequential Resource Allocation for Panel-Based LIS Surfaces
- 2022
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In: 2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings. - 1550-2252. - 9781665482431 ; 2022-June
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Conference paper (peer-reviewed)abstract
- Large intelligent surfaces (LISs) is an evolution of massive MIMO systems allowing for huge capacity gains. To reduce implementation complexity, it is convenient to implement the LIS using panels that are either activated or deactivated, and associated to terminals according to their propagation characteristics to the panels. The associated spatial resource allocation to maximise the terminals', as well as the overall, bit rates can lead to complex optimisation problems.In this paper we consider resource allocation for panel-based LIS surfaces. We present an iterative sequential algorithm for determining the set of active panels and the respective panel-terminal association for the maximisation of the minimum terminal rate. Our algorithm is decentralised and has low complexity. Moreover, it approaches the performance of much more complex, quasi-optimum algorithms.
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| 2. |
- Rimalapudi, Sarvendranath, et al.
(author)
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Physical Layer Abstraction Model for RadioWeaves
- 2022
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In: 2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING). - : IEEE. - 9781665482431 - 9781665482448
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Conference paper (peer-reviewed)abstract
- RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signalto-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.
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| 3. |
- Whiton, Russ, et al.
(author)
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Urban Navigation with LTE using a Large Antenna Array and Machine Learning
- 2022
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In: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring). - 9781665482431
-
Conference paper (peer-reviewed)abstract
- Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block.
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