Joint resource allocation for cognitive OFDM-NOMA systems with energy harvesting in green IoT

Na, Zhenyu; Wang, Xin; Shi, Junren; Liu, Chungang; Liu, Yue; Gao, Zihe

doi:10.1016/j.adhoc.2020.102221

Cited by 18 publications

(7 citation statements)

References 22 publications

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“…The performance of these two estimators depends on the number of pilot symbols. [37][38][39][40][41][42][43][44] Through simulations, the D/L NOMA receiver's performance is investigated considering the effect of the number of pilot symbols.…”

Section: Simulation Resultsmentioning

confidence: 99%

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Pandya

Wakchaure²,

Shankar³

et al. 2021

Journal of Defense Modeling & Simulation

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In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.

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Section: Simulation Resultsmentioning

confidence: 99%

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Pandya

Wakchaure²,

Shankar³

et al. 2021

Journal of Defense Modeling & Simulation

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“…Early corporative multi channel sensing consumes more energy, to sense the spectrums and decide a vacant spectrum in the network. The authors in [29] applies non-orthogonal multiple access (NOMA) to cognitive orthogonal frequency-division multiplexing (OFDM) architecture in order to enhance the IoT nodes lifetime. To harvest energy from radio signals, power splitting mode is applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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Wireless Sensor Networks (WSNs) can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty bands when required. In order to keep the IoT nodes functional all time, continuous energy is required. For this reason the energy harvested techniques are preferred in IoT networks. Mainly it is preferred to harvest Radio Frequency (RF) energy in the network. In this paper a region based multi-channel architecture is proposed. In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region. The Secondary User (SU) that are the licensed user is IoT enabled with Cognitive Radio (CR) techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy. To harvest energy efficiently and to reduce the energy consumption during sensing, the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel. From the experimental analysis, it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel. We also show that channel load can be highly reduced and channel utilization is proved to be more proficient. Thus, this proves the proposed architecture cost-effective and energy-efficient.

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“…In the paper, CR users use the unoccupied bands which are adjacent to the PU subcarriers. Resource allocation in OFDM-based underlay and overlay CR network is discussed in [42] with the concept of simultaneous wireless information and power transfer (SWIPT) adopted in the view of improving the energy efficiency of the system. However, outage analysis in the underlay mode and analysis under selective fading channel conditions are not reviewed.…”

Section: Introductionmentioning

confidence: 99%

Outage and Error-Rate Analysis of NOMA-Inspired Multicarrier Underlay Cognitive Radio System

Shachi¹,

Sanjana²,

Sudhindra³

et al. 2022

IEEE Access

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In this paper, the performance of a multicarrier underlay cognitive radio system is analyzed considering that the successive interference cancellation (SIC) method is used for detection. In the proposed system model, a multicarrier-based secondary user (SU) uses all the subcarriers of the primary user (PU) in underlay mode, guaranteeing the quality of service (QoS) to the primary system. The power allocation strategy is modified accordingly. With a fixed PU transmit power, power is allocated to the SU subcarriers to maximize its throughput while ensuring minimum throughput to PU. The system is analyzed for its performance considering an L-tap multipath Rayleigh fading channel. Analytical expression for outage probability at SU is derived in closed-form. Also, analytical expressions and closed-form approximations for average symbol error rates (SER) per subcarrier of PU and SU are obtained. The derived expressions are validated with the Monte-Carlo (MC) simulations. Further, the proposed scheme was implemented in an orthogonal frequency division multiplexing (OFDM)-based system with different detection schemes used at the receiver. SERs were compared through extensive simulations and it was noticed that SIC with channel estimation based on minimum-mean-squared-error (MMSE) often performed better than that based on least squares (LS) estimation. Observations reveal that the nominal error rates were achieved on the optimal choice of the parameters. The proposed system ensures sufficient throughput to SU, meanwhile eliminating the need for opportunistic sensing.INDEX TERMS NOMA, outage probability, power allocation, SER, underlay cognitive radio.

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Joint resource allocation for cognitive OFDM-NOMA systems with energy harvesting in green IoT

Cited by 18 publications

References 22 publications

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Outage and Error-Rate Analysis of NOMA-Inspired Multicarrier Underlay Cognitive Radio System

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