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In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/ combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature.
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This content will become publicly available on August 8, 2026
Prediction of the Power of Low-Power Networks Using Inertial Sensors
Energy-efficient IoT sensor nodes enable scalable monitoring of diverse physical environments, some of which are exposed to extreme and harsh operating conditions (such as heavy rain or strong movement). For reliable operation of such devices, certain variables must be adaptively adjusted. One of these variables is the transmission power of outgoing packets. In this work, we experimentally investigate how the movement of different bodies of water affects fluctuations in link quality and propose a model for predicting the received power. Once the received power is predicted, a transmitting node can adjust the transmission power to bring the received power to a desired level. Our model is based on minimum mean square estimation (MMSE) and leverages the received power statistics and the movement experienced by the nodes during communication. A disadvantage of MMSE is its dependence on matrix inversion, which is computationally intensive and difficult to implement on resource-constrained devices. We avoid this step and estimate the model parameters using gradient descent, which is much easier to implement. The model achieves an average prediction accuracy of 91% (SD = 1.7%) even with a small number of iterations.
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- Award ID(s):
- 2208392
- PAR ID:
- 10638643
- Publisher / Repository:
- Institute of Electrical and Electronics Engineers (IEEE)
- Date Published:
- Journal Name:
- IEEE Transactions on Instrumentation and Measurement
- Volume:
- 74
- ISSN:
- 0018-9456
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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