Browsing by Author "Poor, H. Vincent"

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Power Allocation for Artificial-Noise Secure MIMO Precoding Systems
(2014-07-01)
Tsai, Shang-Ho
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Poor, H. Vincent
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電機工程學系
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Department of Electrical and Computer Engineering
This paper investigates the power allocation problem for artificial noise (AN) secure precoding systems, and proposes closed-form solutions for maximizing the achievable secrecy rate. It is assumed that the transmitter knows the full channel information at the legitimate receiver, and knows only the statistics of the channel information at the eavesdropper. Lower bounds are derived for the secrecy rates in multiple-input single-output channels with single or multiple eavesdroppers and multiple-input multiple-output channels with multiple eavesdroppers. When the number of transmit antennas is sufficiently large, the bounds are tight, and closed-form solutions can be derived from these bounds. The analytical results suggest simple and yet informative solutions as follows: Let the numbers of receive antennas at the legitimate receiver and at the eavesdropper be N-r and N-r,N-e, respectively. The system should distribute N-r,N-e/(N-r + N-r,N-e) of the power to AN in the high SNR regime, and distribute zero power to AN in the low SNR regime; the rate loss due to the eavesdropper is -N-r log (N-r/(N-r + N-r,N-e)) - N-r,N-e log(N-r,N-e/(N-r + N-r,N-e)) bits/sec/Hz in the high SNR regime and nearly negligible in the low SNR regime. The derived results also show that equal power and water-filling power allocations lead to similar solutions and rate loss. Simulation results corroborate the theoretical results.
Reinforcement Learning-Based Downlink Interference Control for Ultra-Dense Small Cells
(2020-01-01)
Xiao, Liang
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Zhang, Hailu
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Xiao, Yilin
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Wan, Xiaoyue
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Liu, Sicong
The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.