Network control and rate optimization for multiuser MIMO communications

Erpek, Tugba; Sagduyu, Yalin E.; Shi, Yi; Ponnaluri, Satya

doi:10.1016/j.adhoc.2018.10.016

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Erpek et al proposed that the computing and transmission resources should be optimally scheduled for wireless communication packets, and the energy consumption of UE should be the lowest to achieve the optimization. However, the energy of MEC is still unlimited here [22]. Khelladi et al proposed that the scheduler keeps track of the extra bandwidth, which is continuously occupied by terminals/streams with good channel status.…”

Section: Related Workmentioning

confidence: 99%

Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory

Zou

Shen

2022

Mobile Information Systems

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Due to the instability of the wireless channel, data packet loss occurs during wireless communication information transmission. Wireless communication can effectively improve transmission efficiency, study the optimal transmission method of wireless communication data packets, improve the information transmission efficiency and quality of wireless communication, and realize the optimal transmission of wireless communication data packets in order to ensure the reliable transmission of data. It is crucial for increasing wireless communication quality. Natural scheduling and priority scheduling are the only two types of wireless resource scheduling algorithms available so far. These scheduling algorithms are unable to solve the problem of executing the best scheduling under the same priority constraints. This problem is solved using the knapsack theory data knapsack filling procedure. The optimal scheduling of wireless communication packets is investigated in this study using the knapsack theory. The number of destination nodes grows from two to sixteen over time, two at a time. Set the system parameters to a total of 50 original data packets, a packet loss rate of 0.4 in the optimization process, and a packet loss probability of 0.26 in the feedback connection. The KFA approach employed in this study has the highest optimization rate in the three experiments, which can be seen to reduce data packet loss, according to the simulation test. Hybrid coding of data packet transmission is used in conjunction with the buffer feedback method to increase data transmission security. At the same time, it gives practical data for wireless reactive power coded data packet transfer using the time complexity measurement algorithm.

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Section: Related Workmentioning

confidence: 99%

Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory

Zou

Shen

2022

Mobile Information Systems

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“…Note that this formulation can be extended to include interference effects and provide the capability of distributed interference cancellation (such as studied in [31]). We compare the improvement in data rate relative to a long-haul point-topoint link without coherent communications (such as studied in [32]- [34]). We set the coherence time to be 100 milliseconds.…”

Section: B Coherent Communication Gainmentioning

confidence: 99%

Coherent Communications in Self-Organizing Networks With Distributed Beamforming

Shi

Sagduyu

2020

IEEE Trans. Veh. Technol.

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Coherent communications aim to support higher data rates and extended connectivity at lower power consumption compared with traditional point-to-point transmissions. The typical setting of coherent communication schemes is based on a single data stream with multiple transmitters and a single receiver. This paper studies the case of multiple concurrent data streams, each with multiple transmitters and multiple receivers, in self-organizing wireless networks. A distributed optimization solution based on joint network formation and beamforming is developed for coherent group communications in a network of nodes that need to communicate over long distances. This solution significantly improves the power gain for a single data stream and the signal-to-interference-ratio for multiple data streams compared to point-to-point communications. These gains are in turn translated to improvements in communication range, power efficiency, reliability, and throughput. In this multi-layer network optimization solution, nodes coordinate with each other in a distributed manner to form transmitter and receiver groups, and communicate with each other coherently over long distances. The coherent beamforming protocol is optimized for given transmitter and receiver groups, whereas the network formation protocol is optimized to determine these groups. By using single-antenna communication nodes, the proposed optimization solution provides a major gain in network communications and outperforms other benchmark combinations of beamforming and network formation protocols.

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“…where ρ τ is the SNR during the training phase and T τ is the number of training samples. (2) was used in [30], [31] for closed-loop MIMO systems with both channel estimation and feedback (from receiver to transmitter) to perform channel-guided precoding at the transmitter. Different error variances are introduced to the channels (originally used for training) in test time to measure the impact of channel estimation error.…”

Section: B Multiple Antenna Systemsmentioning

confidence: 99%

Deep Learning for Wireless Communications

Erpek

O’Shea

Sagduyu

et al. 2019

Studies in Computational Intelligence

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124

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Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems in utilizing limited spectrum resources. In this chapter, we first describe how deep learning is used to design an end-to-end communication system using autoencoders. This flexible design effectively captures channel impairments and optimizes transmitter and receiver operations jointly in single-antenna, multiple-antenna, and multiuser communications. Next, we present the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks. Deep learning improves the performance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning in providing novel means to design, optimize, adapt, and secure wireless communications.

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Network control and rate optimization for multiuser MIMO communications

Cited by 6 publications

References 10 publications

Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory

Optimal Scheduling Algorithm of Wireless Communication Packets Based on Knapsack Theory

Coherent Communications in Self-Organizing Networks With Distributed Beamforming

Deep Learning for Wireless Communications

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