In this article, the throughput of reconfigurable intelligent surfaces (RIS) is improved using adaptive modulation and coding (AMC). The best modulation and coding scheme (MCS) is selected at the transmitter using the...
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In this article, the throughput of reconfigurable intelligent surfaces (RIS) is improved using adaptive modulation and coding (AMC). The best modulation and coding scheme (MCS) is selected at the transmitter using the instantaneous signal to noise ratio (SNR) measured at the receiver and sent back on the feedback channel. RIS is placed between the transmitter and the receiver so that the SNR is maximised at the receiver side as RIS reflections are combined coherently at the receiver and results in significant spatial diversity. All reflections on RIS have a zero phase at the receiver. We show that RIS using AMC offers a larger throughput than conventional RIS with fixed MCS for all SNR range. RIS with AMC offers 48, 42, 36, 30, 24, 18 dB gain for N = 256, 128, 64, 32, 16, 8 reflectors. We obtained 10 dB gain when RIS uses AMC versus 256-QAM. We also improve the throughput of Simultaneously Transmitting And Reflecting RIS (STARRIS) using AMC. We obtained 13 dB gain when STARRIS uses AMC versus 256-QAM.
As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base stati...
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As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.
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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