Backscatter Sensors Communication for 6G Low-Powered NOMA-Enabled IoT Networks Under Imperfect SIC

Ahmed, Manzoor; Khan, Wali Ullah; Ihsan, Asim; Li, Xingwang; Li, Jianbo; Tsiftsis, Theodoros A.

doi:10.1109/jsyst.2022.3194705

Cited by 33 publications

(26 citation statements)

References 35 publications

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“…Thus, common convex algorithms are not able to handle it. Hence, we split it into two sub-problems, namely P1 w (19) and P1 ρ (20). We then employ alternating optimization (AO) and solve these iteratively using the fractional programming (FP) technique.…”

Section: B Problem Statement and Contributionsmentioning

confidence: 99%

NOMA-Assisted Symbiotic Backscatter: Novel Beamforming Designs Under Imperfect SIC

Rezaei¹,

Galappaththige²,

Tellambura³

et al. 2023

Preprint

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Optimal beamforming designs under imperfect successive interference cancellation (SIC) decoding for a symbiotic network of non-orthogonal multiple access (NOMA) primary users and a secondary ambient tag have been lacking. We address that issue here. The primary base station (BS) serves NOMA users and a passive tag simultaneously in this network. We develop two transmit beamforming designs to meet the user and tag requirements while mitigating the effect of imperfect SIC. Specifically, we design optimal BS transmit beamforming and power allocation to either maximize the weighted sum rate of NOMA users and the tag or minimize the BS transmit power under the minimum rate requirements while satisfying the tag's minimum energy requirement. Because both these problems are non-convex, we propose algorithms using alternative optimization, fractional programming, and semi-definite relaxation techniques. We also analyze their computational complexity. Finally, we present extensive numerical results to validate the proposed schemes and to show significant performance gains while keeping the tag design intact. For example, the proposed digital beamforming increases the harvested power and data rate by 2.16×10 3 % and 314.5 % compared to random beamforming.

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Section: B Problem Statement and Contributionsmentioning

confidence: 99%

NOMA-Assisted Symbiotic Backscatter: Novel Beamforming Designs Under Imperfect SIC

Rezaei¹,

Galappaththige²,

Tellambura³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The primary purpose of these sensor nodes is to collect a large amount of data generated from real-time applications and use it to manage traffic control systems [19], security [20], surveillance [21], and noise pollution control [22], as well as to assist existing infrastructure. Similarly, the amount of data generated grows exponentially as IoT devices such as sensors, actuators, and smartphones become more widely used [23,24]. These devices collect data from various applications, such as health care, monitoring, and security [25,26].…”

Section: Introductionmentioning

confidence: 99%

On Computational Offloading in Massive MIMO-Enabled Next-Generation Mobile Edge Computing

AlJubayrin

Khan

et al. 2022

Wireless Communications and Mobile Computing

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Next-generation wireless communication networks are expected to support massive connectivity with high data rate, low power consumption, and computational latency. However, it can significantly enhance the existing network complexity, which results in high latency. To ease this situation, mobile edge cloud and massive multiple input and multiple output (MIMO) have recently emerged as the effective solutions. Mobile edge cloud has the ability to overcome the constraints of low power and finite computational resources in next-generation communication systems by allowing devices to offload their extensive computation to maximize the computation rate. On the other hand, MIMO can enhance network spectral efficiency by using large number of antenna elements. The integration of mobile edge cloud with massive MIMO also helps to increase the energy efficiency of the devices; as a result, more bits are computed with minimal energy consumption. In this work, a mathematical model is formulated by considering the devices’ energy constraint, which is nonconvex in nature. Following that, to overcome this, we transformed the original optimization problem using the first approximation method and solved the partial offloading schemes. Results reveal that the proposed scheme outperforms the others by considering computational rate as a performance matrix.

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“…The Internet of Things (IoT) has offered us many new sensor applications that are aimed at making our lives easier [ 1 , 2 ]. The idea of the IoT necessitates the deployment of a large number of low-cost, low-power-consumption, and low- to moderate-range sensors [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

Hussain

Alam

Ghauri

et al. 2022

Sensors

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Automatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.

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Backscatter Sensors Communication for 6G Low-Powered NOMA-Enabled IoT Networks Under Imperfect SIC

Cited by 33 publications

References 35 publications

NOMA-Assisted Symbiotic Backscatter: Novel Beamforming Designs Under Imperfect SIC

NOMA-Assisted Symbiotic Backscatter: Novel Beamforming Designs Under Imperfect SIC

On Computational Offloading in Massive MIMO-Enabled Next-Generation Mobile Edge Computing

Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

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