Particle Swarm Optimization-Based Secure Computation Efficiency Maximization in a Power Beacon-Assisted Wireless-Powered Mobile Edge Computing NOMA System

Garcia, Carla E.; Camana, Mario R.; Koo, Insoo

doi:10.3390/en13215540

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…In the partial mode, computational tasks are divided into two segments: one segment is processed locally on the mobile device while the other is transferred to a nearby mobile edge computing server for execution. Conversely, in the binary mode, the entire task is either completed locally on the device or transferred entirely to a nearby mobile edge computing server via the uplink connection [12] [27]. In terms of future directions, an interest approach to explore is to joint task offloading and resource optimization in NOMAbased vehicular networks [27][28] with edge computing AI technology.…”

Section: Future Workmentioning

confidence: 99%

“…Indeed, the IoT's rapid advances for 5G and beyond wireless networks must accommodate the massive connectivity demands imposed by the rapid growth in IoT devices. However, this reality introduces a spectrum scarcity issue, which can be dealt with through the adoption of a NOMA transmission strategy that operates in the power domain and employs techniques like superposition coding and successive interference cancellation [12]. Thus, motivated by the benefits provided by the NOMA technique and next-generation communication systems envisioned to be task-oriented, in this paper, we investigate a low-complexity design to optimize the learning error and power allocation for task-oriented communications in an edge learning NOMA network.…”

Section: Introductionmentioning

confidence: 99%

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ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

García,

Camana,

Koo

2024

IEEE Access

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Conventional communications systems centered on data prioritize maximizing network throughput using Shannon's theory, which is primarily concerned with securely transmitting the data despite limited radio resources. However, in the realm of edge learning, these methods frequently fall short because they depend on traditional source coding and channel coding principles, ultimately failing to improve learning performance. Consequently, it is crucial to transition from a data-centric viewpoint to a taskoriented communications approach in wireless system design. Therefore, in this paper, we propose efficient communications under a task-oriented principle by optimizing power allocation and edge learning-error prediction in an edge-aided non-orthogonal multiple access (NOMA) network. Furthermore, we propose a novel approach based on the ant colony optimization (ACO) algorithm to jointly minimize the learning error and optimize the power allocation variables. Moreover, we investigate four additional benchmark schemes (particle swarm optimization, quantum particle swarm optimization, cuckoo search, and butterfly optimization algorithms). Satisfactorily, simulation results validate the superiority of the ACO algorithm over the baseline schemes, achieving the best performance with less computation time. In addition, the integration of NOMA in the proposed task-oriented edge learning system obtains higher sum rate values than those achieved by conventional schemes.INDEX TERMS task-oriented communication, edge learning, non-orthogonal multiple access (NOMA), learning error, ant colony optimization (ACO).

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Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

García,

Camana,

Koo

2024

IEEE Access

Self Cite

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“…Kan et al formulated the multi-device resource-allocation offloading decision problem and proposed a heuristic algorithm to solve the cost minimization problem [23]. With the technique of energy harvesting, the computation offloading problem for wireless powered WSD networks is also investigated [24][25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Wang

2022

Sensors

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Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.

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“…Thus, high-band technology has been adopted for mmWave communications [15]. Different concepts and strategies have been proposed in recent studies to improve the capacity of wireless transmissions, such as beamforming designs, resource allocation schemes, and multiple antenna-based schemes [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Orbital Angular Momentum-Based Multiple-Input-Multiple- Output with Receive Antenna Shift Keying for 6G

2021

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Spectral efficiency is a major concern for future 6G wireless communication systems. Thus, an appropriate scheme is needed to provide channel capacity improvement for multiple transmitters and receiver-based wireless communication systems without consuming extra resource for communication (e.g., frequency/time/code) or causing interference. Therefore, to fulfill the mentioned requirements for the future 6G wireless network, orbital angular momentum-based multiple-input-multiple-output (OAM-MIMO) multiplexing technique is incorporated with the receive antenna shift keying (RASK) technique in this study (termed as the OAM-MIMO-RASK scheme). OAM-MIMO-RASK can transfer multiple symbols from multiple transmitters to different receivers simultaneously by using multiple subchannels using the OAM and RASK techniques without any interference or additional resource (frequency/time/code). The numerical results illustrated that the proposed OAM-MIMO-RASK can achieve almost double capacity than the existing OAM-MIMO scheme and significantly higher capacity than the existing RASK-based scheme for different values of signal-to-noise ratio. Moreover, the simulation result is validated by the theoretical result which is also shown by the numerical result. In addition, due to different normalized distances from the transmitters and receivers, the proposed OAM-MIMO-RASK scheme can achieve almost double capacity than the existing OAM-MIMO scheme by using OAM-MIMO and RASK technique effectively which is also depicted by the numerical results.

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Particle Swarm Optimization-Based Secure Computation Efficiency Maximization in a Power Beacon-Assisted Wireless-Powered Mobile Edge Computing NOMA System

Cited by 12 publications

References 32 publications

ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

Computation Offloading Game for Multi-Channel Wireless Sensor Networks

Orbital Angular Momentum-Based Multiple-Input-Multiple- Output with Receive Antenna Shift Keying for 6G

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