A class of scalable feedback algorithms for beam and null-forming from distributed arrays

Goguri, Sairam; Peiffer, Ben; Mudumbai, Raghuraman; Dasgupta, Soura

doi:10.1109/acssc.2016.7869616

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…In general, Aggregate Rich Feedback methods are more scalable than Explicit ones. For example, the algorithm proposed in [70] is an Aggregate Rich Feedback method based in 1BF that allows each collaborating node to estimate its channel response to the receiver. Similarly, in the scheme proposed in [71], all the distributed nodes simultaneously transmit, and the receiver sends a phase compensation vector to achieve distributed transmit nullforming.…”

Section: ) Closed-loop Synchronization Methodsmentioning

confidence: 99%

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

et al. 2022

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Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.

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Section: ) Closed-loop Synchronization Methodsmentioning

confidence: 99%

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In general, Aggregate Rich Feedback methods are more scalable than Explicit ones. For example, the algorithm proposed in [73] is an Aggregate Rich Feedback method based in 1BF that allows each collaborating node to estimate its channel response to the receiver. Similarly, in the scheme proposed in [71], all the distributed nodes simultaneously transmit, and the receiver sends a phase compensation vector to achieve distributed transmit nullforming.…”

Section: ) Closed-loop Synchronization Methodsmentioning

confidence: 99%

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Marrero¹,

Duncan²,

Querol³

et al. 2022

Preprint

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CDSS is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSS. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSS. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for DSS. First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSS in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on ML. Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.

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“…Though much of the distributed beamforming works simply focus on achieving a desired SNR at the receiver, sophisticated distributed array techniques such as null-forming has also shown to be achieved through distributed beamforming as a solution to the covert communication problem [3], [4]. However, null-forming is a formidable problem due to its sensitivity to small phase errors.…”

mentioning

confidence: 99%

Multi-Agent Coordination for Distributed Transmit Beamforming

George¹,

Parayil²,

Bai³

2019

Preprint

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This paper presents the formulation and analysis of a two time-scale optimization algorithm for multiagent coordination for the purpose of distributed beamforming. Each agent is assumed to be randomly positioned with respect to each other with random phase offsets and amplitudes. Agents are tasked with coordinate among themselves to position themselves and adjust their phase offset and amplitude such that they can construct a desired directed beam. Here we propose a two time-scale optimization algorithm that consists of a fast time-scale algorithm to solve for the amplitude and phase while a slow time-scale algorithm to solve for the control required to re-position the agents. The numerical results given here indicate that the proposed two time-scale approach is able to reconstruct a desired beam pattern. I. INTRODUCTIONDistributed beamforming is concerned with the problem of cooperative communication where randomly located independent nodes coordinate among themselves to form a virtual antenna array. Although numerous studies on distributed beamforming have been carried out for over a decade, it was initially considered impractical due to the high complexity involved in modeling the generated beam pattern and the hardly achievable requirements on positioning and synchronization. Recent research results demonstrating the efficacy of distributed beamforming as a suitable solution for 5G communication systems such as mm-wave communication and machine to machine communications has further ignited the interest in this research field. The concept of distributed beamforming was conceived in early 2000s by two independent pieces of research under the names collaborative beamforming [1] and distributed beamforming [2].While initial research on collaborative beamforming focused on the beampattern analysis and the random Jemin George and Anjaly Parayil are with the

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A class of scalable feedback algorithms for beam and null-forming from distributed arrays

Cited by 7 publications

References 14 publications

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Multi-Agent Coordination for Distributed Transmit Beamforming

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