Link adaptation is a promising tool of modern networks to combat the time-variant quality of channels. Mod-ulation and codingscheme (MCS) selection is essentially used for link adaptation with channel dynamism. Howev...
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Link adaptation is a promising tool of modern networks to combat the time-variant quality of channels. Mod-ulation and codingscheme (MCS) selection is essentially used for link adaptation with channel dynamism. However, future generation networks need flexible link adaptation schemes that consider more parameters to improve the network performance. This paper proposes an energy-efficient link adaptation algorithm, in which a Deep Reinforcement Learning (DRL) agent is used to find the best match between the channel condition and the link parameters. Also, the downlink transmission power has been considered as a link parameter in addition to the modulation order and coding rate to make the link adaptation more flexible and efficient. Simulation results show that the proposed algorithm outperforms the benchmark algorithms regarding energy efficiency and link throughput.
In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of ...
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In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of the ambient sources employing orthogonal frequency division multiplexing (OFDM) modulation to mitigate strong direct-link interference and improve signal detection of AmBC at the reader. Also, utilization of the sparsity originated from the duty-cycling operation of batteryless RF tags is proposed to increase the dimension of signal space of backscatter signals to achieve either diversity or multiplexing gains in AmBC. We propose optimal constellation mapping and reflection coefficient projection and expansion methods to effectively construct multi-dimensional constellation for high-order backscatter modulation while guaranteeing sufficient energy harvesting opportunities at these tags. Simulation results confirm the feasibility of the long-range and high-rate AmBC in massive IoT networks where a huge number of active ambient sources and passive RF tags coexist.
This paper investigates the throughput performance using adaptive modulation and coding (AMC) focusing on the causes of impairment to Orthogonal Frequency Division Multiplexing (OFDM) - Multiple-Input Multiple-Output ...
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ISBN:
(纸本)9781479944811
This paper investigates the throughput performance using adaptive modulation and coding (AMC) focusing on the causes of impairment to Orthogonal Frequency Division Multiplexing (OFDM) - Multiple-Input Multiple-Output (MIMO) multiplexing. We first investigate the influence of a limited number of modulation and coding schemes (MCSs) and channel estimation error for MCS selection based on mutual information (MI). Second, we clarify the effect of MCS selection error on an increasing maximum Doppler frequency due to the round trip delay time (RTT) in comparison to the throughput upper bound that is computed from the MCS combination providing the maximum throughput considering the block error. Third, we clarify the effect of channel estimation error of maximum likelihood detection (MLD) when using reference signal (RS) based channel estimation in comparison to ideal channel estimation. Through the evaluations, we clarify the impairment factors that degrade the achievable throughput for OFDM-MIMO multiplexing using AMC in an actual multipath fading channel.
Wireless Body Area Networks (WBAN) are integral to the application framework of the Internet of Things (IoT) domain, especially in applications demanding efficient connectivity and availability. Our study introduces a...
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